Multi-unit process spatial synchronization of image inspection systems

ABSTRACT

A conversion control system is described that includes a database to store data defining a set of rules and an interface to receive local anomaly information from a plurality of different analysis machines associated with a plurality of manufacturing process lines that perform a plurality of operations on a web of material, and each of the manufacturing process lines includes position data for a set of regions on the web containing anomalies. The system also includes a computer that registers the position data of the local anomaly information for the plurality of manufacturing process lines to produce aggregate anomaly information. The system further includes a conversion control engine that applies the rules to the aggregate anomaly information to determine which anomalies represent actual defects in the web for a plurality of different products.

TECHNICAL FIELD

The present invention relates to automated inspection of systems, andmore particularly, to inspection of continuously moving webs.

BACKGROUND

Inspection systems for the analysis of moving web materials have provencritical to modern manufacturing operations. Industries as varied asmetal fabrication, paper, non-wovens, and films rely on these inspectionsystems for both product certification and online process monitoring.One major difficulty in the industry is related to the extremely highdata processing rates required to keep up with current manufacturingprocesses. With webs of commercially viable width and web speeds thatare typically used and pixel resolution that is typically needed, dataacquisition speeds of tens or even hundreds of megabytes per second arerequired of the inspection systems. It is a continual challenge toprocess images and perform accurate defect detection at these datarates.

In addition, web process manufacturing operations are becoming morecomplicated with multiple unit operations being performed on a singleroll of material during its production. For example, certain complexweb-based products, such as flexible circuits, may require as many asfifteen distinct manufacturing operations over the course of days oreven weeks, often utilizing multiple production lines at differentphysical sites. In these circumstances, it is typical to collect the webinto a roll after each process and ship the roll to a different locationwhere it is then unrolled, processed, and again collected into a roll.Each process may introduce new anomalies into a web which may or may notcause the web to be defective. Moreover, subsequent processes may makedetection of earlier anomalies difficult, if not impossible.

SUMMARY

In general, techniques are described for the automated inspection ofmoving webs. More specifically, the techniques described herein aredirected to performing spatial registration and combination of anomalydata collected throughout the production of a web. That is, thetechniques provide for the spatial registration and combination ofanomaly data collected throughout multiple unit operations beingperformed on a roll of material during its production, even thoughproduction may require use of multiple production lines over an extendedperiod of time at different physical sites.

For example, during each manufacturing process for the web, one or moreinspection systems acquire anomaly information for the web. Theinspection systems may analyze this so called “local” anomalyinformation and perform a preliminary examination. Image informationabout any regions of the web containing anomalies is stored forsubsequent processing. Similar techniques are applied at each processwithin the multi-process production of the web, thereby generating localanomaly information for each of the manufacturing processes, i.e.,stages.

The anomaly information generated during the various productionprocesses for the moving web may be communicated to a system, where theanomaly information from the different processes for the web can bespatially registered. That is, the respective anomaly information fromthe different processes can be aligned such that the anomalies from thedifferent manufacturing processes have spatial relevance with each otherto produce “aggregate” anomaly information for the web.

The local anomaly data produced by each manufacturing process for a webcan be stored and reconciled with newly acquired anomaly data such thatthe positions of all anomalies detected at all stages of web processingcan be analyzed at a later time. Once aggregated, more sophisticatedalgorithms can be applied to the aggregate anomaly information todetermine any actual defects based on a variety of factors. For example,a conversion control system may subsequently apply one or more defectdetection algorithms to the aggregate anomaly data to ultimatelygenerate a conversion plan for a web roll. That is, the conversioncontrol system may select a conversion plan having defined instructionsfor processing the web roll. The defect detection algorithms applied bythe conversion control system may be application-specific, i.e.,specific to different potential products, to provide for increased oroptimal utilization of the web roll based on the aggregate anomaly data.The conversion control system may communicate this aggregate anomalyinformation and the conversion plan to one or more conversion sites forproducing products from the web.

The use of spatially registered anomaly information that spans multiplemanufacturing processes for a single web may provide many advantages,such as significantly enhanced process quality analysis and control,defective product containment, increased utilization of the web, reducedcost, increase revenue or profit and a variety of other potentialbenefits.

For example, it may be possible to maintain registration of defectposition within 0-2 mm throughout the entire production process. Asanother example, it may be possible to identify waste-by-cause for eachsub-process. Furthermore, the gathered data may prove useful inoptimizing parts combined from different operations. It may also bepossible to automatically reject defective parts even if the defect isundetectable in the final product.

In one embodiment, the invention is directed to a method comprisingperforming a plurality of operations on a web at a plurality ofmanufacturing process lines, imaging a sequential portion of the web toprovide digital information at each of the manufacturing process lines,processing the digital information to produce local anomaly informationfor each of the manufacturing process lines, wherein the local anomalyinformation for each of the manufacturing process lines includesposition data for a set of regions on the web containing anomalies,registering the position data of the local anomaly information for theplurality of manufacturing process lines to produce aggregate anomalyinformation, analyzing at least a portion of the aggregate anomalyinformation to determine which of the anomalies represent actual defectsin the web, and outputting a conversion control plan.

In another embodiment, the invention is directed to a system including aplurality of manufacturing process lines that perform a plurality ofoperations on a web. The system also includes a plurality of imagingdevices positioned within a plurality of manufacturing process lines,wherein each of the imaging devices sequentially images at least aportion of the web to provide digital information. The system furtherincludes one or more analysis computers to process the digitalinformation to produce local anomaly information for each of themanufacturing process lines, wherein the local anomaly information foreach of the manufacturing process lines includes position data for a setof regions on the web containing anomalies. Moreover, the systemincludes a computer that registers the position data of the localanomaly information for the plurality of manufacturing process lines toproduce aggregate anomaly information. The system also includes aconversion control system that analyzes at least a portion of theaggregate anomaly information to determine which anomalies representactual defects in the web for a plurality of different products.

In another embodiment, the invention is directed to a conversion controlsystem comprising a database storing data defining a set of rules and aninterface to receive local anomaly information from a plurality ofdifferent analysis machines associated with a plurality of manufacturingprocess lines that perform a plurality of operations on a web ofmaterial, wherein each of the manufacturing process lines includesposition data for a set of regions on the web containing anomalies. Theconversion control system also includes a computer that registers theposition data of the local anomaly information for the plurality ofmanufacturing process lines to produce aggregate anomaly information.The conversion control system further includes a conversion controlengine that applies the rules to the aggregate anomaly information todetermine which anomalies represent actual defects in the web for aplurality of different products.

In another embodiment, a method comprises performing a plurality ofoperations on a web at a plurality of manufacturing process lines,generating digital information at each of the manufacturing processlines, processing the digital information to produce local anomalyinformation for each of the manufacturing process lines, wherein thelocal anomaly information for each of the manufacturing process linesincludes position data for a set of regions on the web containinganomalies, registering the position data of the local anomalyinformation for the plurality of manufacturing process lines to produceaggregate anomaly information, analyzing at least a portion of theaggregate anomaly information to determine which of the anomaliesrepresent actual defects in the web, and outputting a conversion controlplan.

In another embodiment, a method comprises performing a plurality ofoperations on a web at a plurality of manufacturing process lines,imaging a sequential portion of the web to provide digital informationat each of the manufacturing process lines, processing the digitalinformation to produce local anomaly information for each of themanufacturing process lines, wherein the local anomaly information foreach of the manufacturing process lines includes position data for a setof regions on the web containing anomalies, registering the positiondata of the local anomaly information for the plurality of manufacturingprocess lines to produce aggregate anomaly information, analyzing atleast a portion of the aggregate anomaly information to determine whichof the anomalies represent defects in the web, and outputting aconversion control plan.

Although discussed primarily with respect to gathering anomalyinformation, the techniques described herein are not limited togathering only anomaly information from which defect information may begenerated. For example, in another embodiment, determination of actualdefect may be performed without first identifying anomalies (i.e.,potential defects), for subsequent analysis. As an example, a methodcomprises performing a plurality of operations on a web at a pluralityof manufacturing process lines, imaging a sequential portion of the webto provide digital information at each of the manufacturing processlines, processing the digital information to produce local defectinformation for each of the manufacturing process lines, wherein thelocal defect information for each of the manufacturing process linesincludes position data for a set of regions on the web containingdefects, registering the position data of the local defect informationfor the plurality of manufacturing process lines to produce aggregatedefect information, and outputting a conversion control plan.

The details of one or more embodiments of the invention are set forth inthe accompanying drawings and the description below. Other features,objects, and advantages of the invention will be apparent from thedescription and drawings, and from the claims.

Definitions

For purposes of the present invention, the following terms used in thisapplication are defined as follows:

“web” means a sheet of material having a fixed dimension in onedirection and either a predetermined or indeterminate length in theorthogonal direction;

“sequential” means that an image is formed by a succession of singlelines, or areas of the web that optically map to a single row of sensorelements (pixels);

“pixel” means a picture element represented by one or more digitalvalues;

“defect” means an undesirable occurrence in a product;

“anomaly” or “anomalies” mean a deviation from normal product that mayor may not be a defect, depending on its characteristics and severity.

“filter” is a mathematical transformation of an input image to a desiredoutput image, filters are typically used to enhance contrast of adesired property within an image;

“application-specific” means defining requirements, e.g., grade levels,based on the intended use for the web;

“yield” represents a utilization of a web expressed in percentage ofmaterial, unit number of products or some other manner;

“products” are the individual sheets (also referred to as component)produced from a web, e.g., a rectangular sheet of film for a cell phonedisplay or a television screen; and

“conversion” is the process of physically cutting a web into products.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram illustrating a global network environment inwhich a conversion control system controls conversion of web material.

FIG. 2 is a block diagram illustrating an exemplary embodiment of a webmanufacturing plant.

FIG. 3 is a block diagram illustrating an exemplary sequence ofprocedures and inspections for a web.

FIG. 4 is an illustration of the web manufacturing data collection andanalysis system.

FIGS. 5A-5B are diagrams illustrating exemplary fiducial marks.

FIG. 6 is a picture of an exemplary fiducial mark reader.

FIG. 7 is a diagram illustrating a web and the changes it may undergo,including the later introduction of new anomalies and masking ofprevious anomalies.

FIGS. 8A and 8B are block diagrams illustrating the aggregation of datain accordance with two exemplary embodiments of the techniques describedherein.

FIG. 9 is a flowchart illustrating the production of a web.

FIG. 10 is a flowchart illustrating the inspection steps of a processline.

FIG. 11 is a flowchart illustrating central reconciliation of datagathered from a plurality of processes in one exemplary embodiment.

FIG. 12 is a flowchart illustrating the steps for reconciling the datagathered from a plurality of processes in an exemplary embodiment.

FIG. 13 is a block diagram illustrating an exemplary embodiment offiducial mark writer.

FIGS. 14A-14D are block diagrams illustrating the positions of existingand inserted fiducial marks.

FIG. 15 is a flowchart illustrating exemplary operations involved inapplication of fiducial marks to a web.

FIG. 16 is a flowchart illustrating exemplary operations involved inidentifying spatially synchronized areas of web material throughoutmultiple manufacturing operations.

FIG. 17 is a flowchart illustrating exemplary operations involved inreducing search space of data associated with particular web rolls.

FIGS. 18A-18B are block diagrams illustrating example web segments withoverlapping fiducial marks.

FIG. 19 is a screenshot illustrating a comparison of data gathered fromtwo process lines.

FIG. 20 is a block diagram that depicts an example of an alternativeembodiment for applying techniques to spatially synchronize data, suchas attribute or anomaly data.

FIG. 21 is a block diagram illustrating an alternative embodiment of thetechniques described herein as applied to a system for collectingmeasurement data from a web.

FIG. 22 is a graphical representation of measurement data gathered fromvarious processing and/or measuring operations.

DETAILED DESCRIPTION

FIG. 1 is a block diagram illustrating a global network environment 2 inwhich conversion control system 4 controls conversion of web material.More specifically, web manufacturing plants 6A-6N (“web manufacturingplants 6”) represent manufacturing sites that produce and ship webmaterial in the form of web rolls 7 between each other and ship finishedweb rolls 10 to converting sites 8A-8N. Web manufacturing plants 6 maybe geographically distributed, and each of the web manufacturing plantsmay include one or more manufacturing process lines (FIG. 3).

In general, web rolls 7 may contain manufactured web material that maybe any sheet-like material having a fixed dimension in one direction andeither a predetermined or indeterminate length in the orthogonaldirection. Examples of web materials include, but are not limited to,metals, paper, wovens, non-wovens, glass, polymeric films, flexiblecircuits or combinations thereof. Metals may include such materials assteel or aluminum. Wovens generally include various fabrics. Non-wovensinclude materials, such as paper, filter media, or insulating material.Films include, for example, clear and opaque polymeric films includinglaminates and coated films.

In order to manufacture a finished web roll 10 which is ready forconversion into products 12, unfinished web rolls 7 may need to undergoprocessing from multiple process lines either within one webmanufacturing plant, for instance, web manufacturing plant 6A, or withinmultiple manufacturing plants. For each process, a web roll is typicallyused as a source roll from which the web is fed into the manufacturingprocess. After each process, the web is typically collected again into aweb roll 7 and moved to a different product line or shipped to adifferent manufacturing plant, where it is then unrolled, processed, andagain collected into a roll. This process is repeated until ultimately afinished web roll 10 is produced.

For many applications, the web materials for each of web rolls 7 mayhave numerous coatings applied at one or more production lines of one ormore web manufacturing plants 6. The coating is generally applied to anexposed surface of either a base web material, in the case of the firstmanufacturing process, or a previously applied coating in the case of asubsequent manufacturing process. Examples of coatings includeadhesives, hardcoats, low adhesion backside coatings, metalizedcoatings, neutral density coatings, electrically conductive ornonconductive coatings, or combinations thereof. A given coating may beapplied to only a portion of the web material or may fully cover theexposed surface of the web material. Further, the web materials may bepatterned or unpatterned.

During each manufacturing process for a given one of web rolls 7, one ormore inspection systems acquire anomaly information for the web. Forexample, as illustrated in FIG. 2, an inspection system for a productionline may include one or more image acquisition devices positioned inclose proximity to the continuously moving web as the web is processed,e.g., as one or more coatings are applied to the web. The imageacquisition devices scan sequential portions of the continuously movingweb to obtain digital image data. The inspection systems may analyze theimage data with one or more algorithms to produce so called “local”anomaly information. The anomaly information may be referred to hereinas local anomaly information in that the anomaly information generallyincludes position information that is specific to a coordinate systemlocal to, or generally used by, the production line currently in use. Asdescribed below, this local position information may be meaningless toother manufacturing plants or even other production lines within thesame manufacturing plant. For these reasons, the local anomalyinformation obtained during the production for each of web rolls 7 isspatially registered with other local anomaly information for the sameweb roll. That is, the position information associated with the localanomaly is translated to a common coordinate system to align positioninformation from different manufacturing processes applied to the sameweb roll 7 or a segment of web roll 7. The anomaly information isreferred to herein as aggregate anomaly information once collected andaligned with anomaly information for at least one or possibly all of themanufacturing processes for the same web roll 7.

More specifically, during each manufacturing process, the imageinformation (i.e., raw pixel information) for any regions of the webcontaining anomalies is stored for subsequent processing. That is, theraw image data surrounding an identified anomaly is extracted from thestream of pixel information obtained from the image acquisition deviceand stored along with position information indicating the specificlocation of the anomaly within the web, both with respect to thedimension across the web and the dimension running the length of theweb. Image data not associated with anomalies is discarded. Similartechniques are applied at each process within the multi-processproduction of a given web roll 7, thereby generating local anomalyinformation for each of the manufacturing processes, i.e., stages.

The local anomaly information generated during the various productionprocesses for the moving web is then communicated to conversion controlsystem 4, where the local anomaly information from the differentprocesses for the web can be spatially registered. That is, therespective anomaly information from the different processes can bealigned such that the anomalies from the different manufacturingprocesses have spatial relevance with each other to produce theaggregate anomaly information for a given web roll 7. Spatialregistration may occur at any time during the overall manufacturingprocess, e.g., between each stage of the multi-process production for aweb roll or after completion of all the processes. Moreover, spatialregistration may be performed centrally, such as within conversioncontrol system 4, or locally at a given web manufacturing plant 6 usingthe local anomaly information obtained from the production linespreviously used for the given web roll 7.

In general, conversion control system 4 applies one or more defectdetection algorithms that may be application-specific, i.e., specific toproducts 12, to select and generate a conversion plan for each web roll10. A certain anomaly may result in a defect in one product, forinstance product 12A, whereas the anomaly is not a defect in a differentproduct, for instance, product 12B. Each conversion plan representsdefined instructions for processing a corresponding finished web roll10. Conversion control system 4 communicates the conversion plans forweb rolls 10 via network 9 to the appropriate converting sites 8 for usein converting the web rolls into products 12.

In order to properly create a conversion plan for converting a finishedweb roll 10 which has undergone multiple manufacturing processes, thedata collected by web manufacturing plants 6 is spatially reconciled andanalyzed to form a composite defect map. As noted above, collectedanomaly data generally includes small regions of raw image data alongwith position information representing the locations of anomalies on aweb roll. Spatial reconciliation of anomaly data can either be done at acentral location, such as conversion control system 4, once allprocesses have finished or at various intermediate process locations.Moreover, a predefined, spatial coordinate system may be used forregistration of the data. In this case, all of the position dataassociated with the local anomaly information is translated to thispredefined coordinate system. As an alternative, a coordinate systemused within a first process (or any other process) applied to a givenweb roll 7 can act as a reference coordinate system to which all localanomaly data is registered for subsequent processes applied to the sameweb roll.

For example, an inspection system for a first manufacturing processapplied to a given web roll 7 can submit its local anomaly informationto conversion control system 4 once the first process has finished. Thismay include coordinate system reference data describing a coordinatesystem utilized by the inspection system while collecting the initiallocal anomaly information. Then, inspection systems or other computingdevices associated with each subsequent manufacturing process applied tothat same web roll 7 may retrieve the coordinate system reference dataused by the first process from conversion control system 4 and adjustthe position data for any newly gathered local anomaly informationaccording to the coordinate system used during the first manufacturingprocess. As mentioned, alternatively, conversion control system 4 mayprocess local anomaly information from each of the manufacturingprocesses. In this manner, all of the position data of the local anomalyinformation gathered from all manufacturing processes for the same webroll 7 can be reconciled so that all anomalous regions in web roll 10are known regardless of when, that is, from which process, each anomalywas introduced.

Conversion control system 4 applies one or more defect detectionalgorithms to the aggregate anomaly information to ultimately select andgenerate a conversion plan for each web roll 10. Conversion controlsystem 4 may select converting sites 8 based on one or more parameters,and ultimately may direct the conversion of web rolls 10 into products12. That is, conversion control system 4 selects, in an automated orsemi-automated manner, converting sites 8 for converting web rolls 10based on one or more site selection parameters, such as current productinventory levels at the various converting sites. Conversion controlsystem 4 may utilize other site selection parameters, such as orderinformation associated with each of products 12 at the variousconverting sites 8, current product demand experienced within thegeographic regions serviced by the converting sites, shipping costs andtransportation options associated with each of the converting sites, andany time-critical orders pending at the converting sites.

Based on the selections made by conversion control system 4, web rolls10 are shipped to converting sites 8A-8N (“converting sites 8”), whichmay be geographically distributed within different countries. Convertingsites 8 convert each web roll 10 into one or more products.Specifically, each of converting sites 8 includes one or more processlines that physically cut the web for a given web roll 10 into numerousindividual sheets, individual parts, or numerous web rolls, referred toas products 12A-12N (“products 12”). As one example, converting site 8Amay convert web rolls 10 of film into individual sheets for use inautomobile lighting systems. Similarly, other forms of web materials maybe converted into products 12 of different shapes and sizes dependingupon the intended application by customers 14A-14N (“customers 14”).Each of converting sites 8 may be capable of receiving different typesof web rolls 10, and each converting site may produce different products12 depending on the location of the converting site and the particularneeds of customers 14.

The use of spatially registered anomaly information that spans multiplemanufacturing processes for a single web may provide many advantages,such as significantly enhanced process quality analysis and control,defective product containment, increased utilization of the web, reducedcost, increase revenue or profit and a variety of other potentialbenefits. For example, it may be possible to maintain registration ofdefect position within 0-5 mm or preferably within 0-2 mm throughout theentire production process. As another example, it may be possible toidentify waste-by-cause for each sub-process. Furthermore, the gathereddata may prove useful in optimizing parts combined from differentoperations. It may also be possible to automatically reject defectiveparts even if the defect is undetectable in the final product.

FIG. 2 is a block diagram illustrating an exemplary embodiment of oneprocess line in an exemplary embodiment of web manufacturing plant 6A ofFIG. 1. In the exemplary embodiment, a segment of a web 20 is positionedbetween two support rolls 22, 24. Image acquisition devices 26A-26N(“image acquisition devices 26”) are positioned in close proximity tothe continuously moving web 20. Image acquisition devices 26 scansequential portions of the continuously moving web 20 to obtain imagedata. Acquisition computers 27 collect image data from image acquisitiondevices 26, and transmit the image data to analysis computer 28 forpreliminary analysis.

Image acquisition devices 26 may be conventional imaging devices thatare capable of reading a sequential portion of the moving web 20 andproviding output in the form of a digital data stream. As shown in FIG.2, imaging devices 26 may be cameras that directly provide a digitaldata stream or an analog camera with an additional analog to digitalconverter. Other sensors, such as, for example, laser scanners may beutilized as the imaging acquisition device. A sequential portion of theweb indicates that the data is acquired by a succession of single lines.Single lines comprise an area of the continuously moving web that mapsto a single row of sensor elements or pixels. Examples of devicessuitable for acquiring the image include linescan cameras such asModel#LD21 from Perkin Elmer (Sunnyvale, Calif.), Piranha Models fromDalsa (Waterloo, Ontario, Canada), or Model Aviiva SC2 CL from Atmel(San Jose, Calif.). Additional examples include laser scanners fromSurface Inspection Systems GmbH (Munich, Germany) in conjunction with ananalog to digital converter.

The image may be optionally acquired through the utilization of opticassemblies that assist in the procurement of the image. The assembliesmay be either part of a camera, or may be separate from the camera.Optic assemblies utilize reflected light, transmitted light, ortransflected light during the imaging process. Reflected light, forexample, is often suitable for the detection of defects caused by websurface deformations, such as surface scratches.

Fiducial mark controller 30 controls fiducial mark reader 29 to collectroll and position information from web 20. For example, fiducial markcontroller may include one or more photo-optic sensors for reading barcodes or other indicia from web 20. In addition, fiducial markcontroller 30 may receive position signals from one or morehigh-precision encoders engaged with web 20 and/or rollers 22, 24. Basedon the position signals, fiducial mark controller 30 determines positioninformation for each detected fiducial mark. For example, fiducial markcontroller 30 may produce position information locating each detectedfiducial mark within a coordinate system applied to the process line.Alternatively, analysis computer 28 may place each of the detectedfiducial marks within the coordinate system based on the position datareceived from fiducial mark controller 30. In this case, the positiondata provided by fiducial mark controller 30 may represent distancesbetween each fiducial mark in a dimension along the length of web 20. Ineither case, fiducial mark controller 30 communicates the roll andposition information to analysis computer 28.

Analysis computer 28 processes image streams from acquisition computers27. Analysis computer 28 processes the digital information with one ormore initial algorithms to generate local anomaly information thatidentifies any regions of web 20 containing anomalies that mayultimately qualify as defects. For each identified anomaly, analysiscomputer 28 extracts from the image data an anomaly image that containspixel data encompassing the anomaly and possibly a surrounding portionof web 20. Analysis computer 28 may classify an anomaly into differentdefect classes if necessary. For instance, there may be unique defectclasses to distinguish between spots, scratches, and oil drips. Otherclasses may distinguish between further types of defects.

Based the position data produced by fiducial mark controller 30,analysis computer 28 determines the spatial position of each anomalywithin the coordinate system of the process line. That is, based on theposition data from fiducial mark controller 30, analysis computer 28determines the x-y and possibly z position for each anomaly within thecoordinate system used by the current process line. For example, acoordinate system may be defined such that the x dimension represents adistance across web 20, a y dimension represents a distance along alength of the web, an the z dimension represents a height of the web,which may be based on the number of coatings, materials or other layerspreviously applied to the web. Moreover, an origin for the x, y, zcoordinate system may be defined at a physical location within theprocess line, and is typically associated with an initial feed placementof the web 20. The coordinate system defined for the current processline may not be (and is typically not) the same coordinate system forany previous or subsequent processes applied to web 20.

In any case, analysis computer 28 records in database 32 the spatiallocation of each anomaly with respect to the coordinate system of theprocess line, this information being referred to herein as local anomalyinformation. That is, analysis computer 28 stores the local anomalyinformation for web 20, including roll information for the web 20 andposition information for each anomaly, within database 32. As describedbelow, the local anomaly information generated for the current processline is subsequently spatially registered with local anomaly informationgenerated by the other process lines for the same web. Database 32 maybe implemented in any of a number of different forms including a datastorage file or one or more database management systems (DBMS) executingon one or more database servers. The database management systems may be,for example, a relational (RDBMS), hierarchical (HDBMS),multidimensional (MDBMS), object oriented (ODBMS or OODBMS) or objectrelational (ORDBMS) database management system. As one example, database32 is implemented as a relational database provided by SQL Server™ fromMicrosoft Corporation.

Once the process has ended, analysis computer 28 will transmit the datacollected in database 32 to conversion control system 4 via network 9.Specifically, analysis computer 28 communicates the roll information aswell as the local anomaly information and respective sub-images toconversion control system 4 for subsequent, offline, detailed analysis.For example, the information may be communicated by way of a databasesynchronization between database 32 and conversion control system 4.

Spatial registration of anomaly data can be performed subsequently atconversion control system 4, either after one or more processes or onceall processes have finished. Alternatively, analysis computer 28 mayperform the spatial registration. For example, in such an embodiment,conversion control system 4 may communicate through network 9 withanalysis computer 28 to inform analysis computer 28 of a coordinatesystem that is to be used for reconciled anomaly data. In this case,analysis computer 28 may spatially register position local anomaly datafor web 20, which is typically based on a coordinate system of thecurrent process line, with the representative coordinate systemspecified by conversion control system. Conversion control system 4 mayselect the representative coordinate system that is to be used forspatial registration based on a coordinate system associated with thefirst manufacturing process line applied to web 20. Alternatively,coordinate system of any other process line used or scheduled to be usedfor web 20 may be selected. Moreover, conversion control system 4 maydefine a coordinate system different from any of the coordinate systemsassociated with the product lines.

As an example, a first manufacturing process may have recorded fiducialmark “38” at a position of 76.027 meters (m) along the length of web 20.The current process, however, may record fiducial mark “38” at 76.038 m,an offset of 0.011 m. Analysis computer 28 (or optionally conversioncontrol system 4 or some other centralized computing device) may adjustmeasurements of position data for the current process to align theposition data with position data from the first process. That is, fromthe example above, analysis computer 28 may translate the position datafor the detected fiducial mark “38” to match the position 76.027 mwithin the first process. Likewise, if analysis computer 28 detects ananomaly at position 76.592 m, analysis computer 28 applied a similardegree of translation to record this anomaly as being present atposition 76.581 m. This translation may, for example, be effected byadjusting the position of the anomaly as measured by the current processaccording to an offset or other translation function determined based onthe current position of fiducial mark “38” and the prior recordedposition of the same fiducial mark. Analysis computer 28 may use thesame offset or other translation function for each fiducial mark andanomaly, or analysis computer 28 may determine a unique offset or othertranslation function for each section of a web occurring between twoconsecutive fiducial marks. That is, analysis computer 28 may determinethat the offset to be applied to anomalies between fiducial marks “38”and “39” is 0.011 m, while the offset to be applied to anomalies betweenfiducial marks “76” and “77” is 0.008 m.

In another embodiment, each process line may gather the local anomalydata independently of all other processes. That is, an analysis computer28 for each manufacturing plant or product line records the positionaldata of fiducial marks and anomalies in database 32 as measured withrespect to the coordinate system of the current process without regardto the position data recorded for the fiducial marks by any otherprocess. Analysis computer 28 transmits this data to conversion controlsystem 4 via network 9. Once all of the processes have finished,conversion control system 4 may reconcile all of the collected data.

As an example, the first process may have recorded fiducial mark “38” ata position of 76.027 m along the length of the web, while a subsequentmanufacturing process applied to the web may have recorded fiducial mark“38” at 76.038 m. Likewise, the subsequent process may have recorded ananomaly at position 76.592 m. Conversion control system 4 may spatiallyregister the fiducial mark “38” measured by the subsequent process bytranslating the position data to match 76.027 m measured during thefirst manufacturing process. Conversion control system 4 may thenperform a similar translation on the position data for the anomalydetected during the subsequent process to record this anomaly as beingpresent at position 76.581 m according to the calculated offset of 0.011mm. As discussed above, conversion control system 4 may use the sameoffset for each fiducial mark and anomaly from each process, orconversion control system 4 may determine a unique offset for eachsection of a web from each process occurring between two consecutivefiducial marks. For example, conversion control system 4 may determinethat the offset between fiducial marks “38” and “39” from process 5 is0.011 m, while the offset between fiducial marks “76” and “77” fromprocess 5 is 0.008 m. Other functions may be used to spatially registerthe data. For example, conversion control system 4 defines a coordinatesystem for use in spatially registering the locally anomaly data,conversion control system 4 may apply one or more mapping functions tomap position data into the coordinate system.

FIG. 3 is a block diagram illustrating an exemplary sequence ofmanufacturing processes 50 applied to a single web. In an exemplaryembodiment, the sequence of manufacturing processes 50 may performnumerous individual manufacturing processes upon web roll 7 by passingweb roll 7 through individual process lines 74A-74Q (“process lines74”). Process lines 74 may be provided by a single manufacturing plant 6or may be located within different manufacturing plants.

In general, each of process lines 74 includes equipment to perform anumber of operations 52 and one or more inspection systems to perform anumber of inspection operations 54. There may be one or more inspectionssystems for each of process lines 74. Alternatively, there may becertain subset of process lines 74 that do not have inspection systems,while the rest of process lines 74 have one or more product inspections.

In an exemplary sequence of processes and inspections, such as thatdepicted in FIG. 3, a web roll 7 may be a plastic film, which may beginon process line 74A where the base film is first formed in accordancewith operation 52A. On this process line, web roll 7 may be unwound andsubjected to an initial inspection 54A. Operation 52A may, for example,clean web roll 7, operation 52B may prime web roll 7, and operation 52Cmay cure web roll 7. Web roll 7 may then be inspected a second time byinspection 54B and then wound into a roll.

Web roll 7 may subsequently be moved or shipped to process line 74B,where web roll 7 is then unwound for feeding into the process line 74B.In this example, operation 52D imparts web roll 7 with an embossedpattern and then 7 an inspection operation 54C is performed before beingcollected into a roll.

Additional manufacturing processes may be performed by subsequentprocess lines, until web roll 7 is shipped to a final process line 74Q,where web roll 7 is again unwound. As examples, operation 52N may coatweb roll 7 with an opaque adhesive, operation 52P may uv-cure web roll 7and laminate web roll 7 to a liner film, where there is one moreinspection 54M before web roll 7 is rewound into the final form as webroll 10. Web roll 10 is then ready to be converted into products 12.

Any one of processes 52 may impart anomalies into web roll 7 that aresubsequently identified as defects. Therefore, it may be desirable toinspect for defects within one or more of the different manufacturingprocess lines 74. For example, as shown in FIG. 3, there may be one ormore inspections 54 for each of process lines 74. By frequentlyinspecting the web, the local anomaly data captured from the inspectionsat each of the process lines can be examined to individually optimizeeach of processes 52. This may allow for identification of the causes ofdefects for expeditious correction.

Moreover, the local anomaly data captured from the inspections at eachof the process lines can later be spatially registered to form aggregateanomaly information that can be used for a variety of purposes. Forexample, the aggregate anomaly information can be examined to furtheroptimize each of processes 52 based on their contribution to the overalldefects in the end products. That is, depending upon the productapplication ultimately selected for the web, some of the operationsperformed by processes 52 may act to eliminate, cover or otherwise actto effectively remove or lessen the effect of an anomaly introduced by aprevious one of the processes. An anomaly introduced into a basematerial of the web, for example, may be subsequently covered bycoatings applied to the web. In addition, some so-called hiddenanomalies may have little or no impact on the ultimate performance ofthe end products. The use of spatially registered aggregate anomalyinformation may allow conversion control system 4 to identify only therelevant anomalies from the multi-process production of a web based on avariety of factors, including the application selected.

FIG. 4 is an illustration of an example embodiment of distributed webmanufacturing system 2 shown in FIG. 1. Specifically, FIG. 4 depicts thecertain elements of the example system FIG. 1 in greater detail. Each ofweb manufacturing plants 6 may comprise one or more process lines 74having inspection systems and analysis computers as shown in FIGS. 2 and3. In addition, each web manufacturing plant, for instance, webmanufacturing plant 6A, may comprise a consolidation server, forinstance, consolidation server 76A.

In some embodiments, a single processing line 74 may perform multipleoperations on a web at various times. For example, process line 74A maybe configured to perform a first operation or set of operations on webroll 7 using a first set of one or more coordinate systems and/orfiducial markings. Once process line 74A has finished the firstoperation, process line 74A may be reconfigured to perform a secondoperation or set of operations, potentially using a second set of one ormore coordinate systems and/or fiducial markings. Web roll 7 may then be“reloaded,” i.e. again placed at the start of process line 74A, and thenprocess line 74A may perform the second operation or set of operationson web roll 7. In this way, a single process line, e.g. process line74A, could potentially perform all necessary operations in theconversion process of web roll 7, and the position data for the firstset of operations and the second set of operations can be spatiallyregistered in accordance with the techniques described herein.

Each web manufacturing plant, for instance, web manufacturing plant 6A,may comprise one or more consolidation servers, for instance,consolidation server 76A for collection and communication of data.Consolidation server 76A may collect data from a respective analysiscomputer 28 of each of processes 74A-74B to transmit to conversioncontrol system 4. Conversion control system 4 may collect and storeglobal data corresponding to web rolls 10 as well as copies of the localanomaly information and the aggregate anomaly information for each ofthe rolls. In one embodiment, consolidation servers 76 assign particular“roll names” to each of web rolls 7. In another embodiment,consolidation servers 76 may assign roll names to segments of web rolls7, 10. In one embodiment, consolidation servers 76 may associate rollnames with particular web rolls or segments of web rolls and particularprocess lines 74; that is, any one web roll 7 may comprise a pluralityof various roll names, each roll name corresponding to a differentprocess line 74. In yet another embodiment, consolidation servers 76 donot assign any roll names to web rolls 7 but only identify web rolls 7according to fiducial marks, for example, a series of fiducial marks asone of the fiducial marks depicted in FIG. 5.

In some embodiments, a consolidation server, for instance, consolidationserver 76A, reconciles anomaly information produced at process lines74B, for example, with the data collected from the first process line,for example, process line 74A, prior to communication to conversioncontrol system 4. In another embodiment, each of consolidation servers76A-76N may store local anomaly information received from each ofprocess lines 74 without registration; conversion control system 4 maysubsequently collect the local anomaly information from each ofconsolidation servers 76A-76N and reconcile all of the data internallywithin conversion control system 4 at a later time to form a compositemap. In yet another embodiment, consolidation server 76A, for example,may receive instruction from conversion control system 4 in order toreconcile on-site any anomaly information generated for a web.

In one example, conversion control system 4 may gather and merge alldata corresponding to each web roll 10 from consolidation servers 76. Inanother example, conversion control system 4 may create metadata whichdescribes the external location of data regarding each web roll 10 (,e.g. by specifying a network address for each of consolidation servers76); conversion control system 4 may later use the metadata to controlmerging of data from each of consolidation servers 76 regarding aparticular web roll 10.

In one example, data may originate from a process line, e.g. processline 74A, of a particular location, e.g. plant 6A. Each web roll 10 maybe assigned an identifier which may describe the product or products forwhich the particular web roll 10 is intended. The identifier may alsouniquely identify the particular web roll 10.

In one example, each of web rolls 10 may undergo a particular “recipe.”A recipe, generally, is a combination or defined sequence of processlines which operate to manipulate the particular web roll 10. Forexample, one recipe may be process line 74A of plant 6A, process line74E of plant 6C, and process line 74Q of plant 6N.

Because web rolls 10 are unwound and rewound at process lines 74,conversion control system 4 may identify the direction in which the rollwas traveling on the process line in order to facilitate merger of thedata. Direction of the web roll may be determined based on analysis ofthe fiducial marks. In one embodiment, for example, fiducial marks maybe a sequence of integers which increment by one for each sequentialfiducial mark; thus it may be possible to determine direction of the webroll (i.e., which end of the roll was fed first into the manufacturingprocess) by analyzing whether the fiducial marks are ascending ordescending.

Once all of the data has been reconciled, conversion control system 4may transmit the composite map and a conversion plan to server 75 ofconverting system 78, such as by using the File Transfer Protocol (FTP)or any other data communications protocol. Web rolls 10 may be shippedto one of converting sites 8A-8N (“converting sites 8”). Convertingsites 8 may utilize the composite map and the conversion plan fromconversion control system 4 in transforming web rolls 10 into products12.

FIG. 5A is a diagram illustrating one embodiment of an example fiducialmark that may be printed or otherwise formed on an individual web. Morespecifically, fiducial marks are placed at regular intervals throughoutthe length of a web (FIG. 7), preferably outside of the salable area ofthe web, in order to accurately locate and uniquely identify a physicallocation on the web. As described herein, the techniques may utilizefiducial marks to enable electronic position data to be accuratelyspatially registered and combined for multiple unit operationscontaining various error sources, production lines and evenmanufacturing plants. In other words, the fiducial marks allow a readerto later detect and record errors relative to the position of thefiducial mark(s). Although shown as including a barcode and otherfeatures, other forms of indicia may server such purposes.

In an embodiment of a fiducial mark as depicted in FIG. 5A, a fiducialmark has one or more locating marks 82, 84 and a barcode 80. Locatingmarks 82, 84 enable a fiducial mark reader to accurately locate theposition of barcode 80. Barcode 80 represents information provided in amachine-readable format. Barcode 80 may, for example, encode a uniqueidentifier for each fiducial mark. Barcode 80 may encode otherinformation, such as position information based on a coordinate systemused when applying the mark, an identifier for the web to which the markhas been applied, designation of production lines used or scheduled tobe used for manufacturing the web, routing information defining a routefor the web through manufacturing process lines and/or manufacturingplants, information identifying the material applied and in which orderand area of the web, environmental conditions measured during theprocess, instructions for downstream processing of the web, and a hostof other information.

In one embodiment, barcode 80 may conform to the interleaved “2 of 5”symbology standard. In one embodiment, barcode 80 may represent a simpleinteger in the range from 0 to 999,999. In one embodiment, each fiducialmark placed on a web is one greater than the previous fiducial mark. Inone embodiment, fiducial marks may be applied to a web using an inkjetprinter. The process of placing fiducial marks on a web is described infurther detail in co-pending application Floeder et al., U.S. App. No.2005/0232475, Apparatus and Method for the Automated Marking of Defectson Webs of Material (published 2005), which is hereby incorporated byreference in its entirety.

Other embodiments may represent fiducial marks in a variety of otherways. For example, data may be represented by a 1D barcode, a 2Dbarcode, optical character recognition (OCR), or magnetically encoded.Furthermore, other embodiments may apply fiducial marks to a web usinginkjet printing, laser printing, or by securing mechanical labels to theweb. Other means of representing a fiducial mark, as well as otherapplication methods, may also be used. Further, fiducial marks need notbe iterating nor periodically spaced, as fiducial marks merely serve asa reference point for anomalies; iterating fiducial marks is merely aconvenient way of producing fiducial marks.

In general, fiducial marks are used to combine electronic data ofanomalies recorded from various inspections. During a firstmanufacturing process, fiducial marks may already be present on the web,preferably near the edge of the web outside of the salable product. Iffiducial marks are not present, the first manufacturing process appliedto the web should apply fiducial marks, e.g., at regular intervals alongthe edge of the web. In one embodiment, each fiducial mark represents aninteger one unit greater than the previous fiducial mark. In oneembodiment, fiducial marks are recorded on the web approximately twometers apart. Precise distance may not be required between fiducialmarks, as fiducial marks serve as a relative indicator of position.

FIG. 5B depicts another example embodiment of a fiducial mark. In thisembodiment, the fiducial mark comprises two locating marks 82, 84 whichare substantially similar in purpose and function as those depicted inFIG. 5A. However, barcode 81 of FIG. 5B is substantially different frombarcode 80 of FIG. 5A and is represented as a compound fiducial markthat includes a barcode 81 having a first mark to representmanufacturing data and a second mark to uniquely identify the fiducialmark. Specifically, in this example, barcode 81 comprises twelve digitsof information in the interleaved 2 of 5 format, six digits in each ofan upper tier and a lower tier. Although discussed with respect to theinterleaved 2 of 5 format, other barcode formats may be used as well. Inthis example, the lower tier digits form a simple integer in the rangefrom 0 to 999,999. The upper tier comprises three pieces of information,a system identifier (ID) indicating the manufacturing process line thatapplied the fiducial mark and a date represented as day and yearindicating when the fiducial mark was applied. The upper tier digits maybe arranged as SSYDDD, and the lower tier digits may be arranged as asix digit integer ######. The contents of exemplary barcode 81 aresummarized in Table 1 below.

TABLE 1 Description Representation # of Digits System ID SS 2 Year Y 1Day of Year DDD 3 Six Digit Identifier ###### 6

The system IDs may be divided among manufacturing plants 6. For example,the system IDs could be distributed as shown in Table 2 below.

TABLE 2 System ID Plant Description 00-04 Plant 6A Plastic Film 05-09Plant 6B Adhesive Coating 10-19 Plant 6C Abrasive Products 20-29 Plant6D Metal Coating 30-79 RESERVED RESERVED 80-99 Plant 6N Film Lamination

The use of multi-tiered barcodes may provide several advantages. Forexample, multi-tiered barcodes are compatible with readers designed toread only a single tiered barcode (e.g., FIG. 6) by simply utilizingmultiple readers. Likewise, this multi-tiered barcode may encompass allinformation needed to uniquely identify all processes and specificsystems across an entire manufacturing chain of operations. Fiducialmarks from different processes may be applied to the same web withoutany loss of information or creating ambiguities. One exemplary methodfor inserting fiducial marks one a moving web is discussed below withrespect to FIG. 15.

FIG. 6 is an illustration of an exemplary fiducial mark reader 29 (FIG.2). In the illustrated example, fiducial mark reader 29 includes a framehaving a barcode reader 85, two fiducial sensors 86A, 86B, and a lightsource 88 mounted thereon. In addition, fiducial mark controller 30,which may be a microcontroller or general processor, may be embeddedwithin fiducial mark reader 29 or coupled to reader 29 by a suitableelectronic data path.

Fiducial mark controller 30 receives signals from fiducial sensors 86Aand 86B and activates bar code scanning upon detecting both locatingmarks 82, 84 of a fiducial mark either simultaneously or within apredefined time period, e.g., 0-10 milliseconds. In this manner,fiducial sensors 86A and 86B are used to determine when the barcode iswithin a read zone associated with barcode reader 85. Fiducial sensors86A and 86B may be photo-optic sensors accompanied by focusing optics.In one embodiment, fiducial locators 82, 84 are printed or otherwiseplaced at a predefined width W apart on the web, and fiducial sensors86A and 86B are mounted on the frame of fiducial mark reader at thewidth W apart in order to substantially simultaneously detect bothlocating marks 82, 84. In one example, the width W is selected to be 100mm.

When both sensors 86A and 86B detect a corresponding locating mark,fiducial mark controller 30 activates light source 88 in order to readbarcode 80 of the fiducial mark. In some embodiments, light source 88may remain lit at all times. In other embodiments, light source 88 maybe illuminated only when both fiducial sensors 86A, 86B detect locatingmarks substantially simultaneously. In one embodiment, when bothfiducial sensors 86A and 86B detect locating marks 82, 84, barcodereader 85 captures an image of barcode 80 rather than processing theimage data to read the bar code in real-time. Fiducial mark controller30 may store the image in database 32, and image data representative ofthe captured barcode 80 may be read and interpreted at some later time.In another embodiment, fiducial mark controller 30 directs barcodereader 85 to capture an image of barcode 80 for processing in real-timeto read the barcode. That is, barcode reader 85 may extract the datafrom the image of barcode 80 and analyze the image data to determine themachine-readable information contained therein.

Once barcode reader 85 has read barcode 80, fiducial mark reader 29 mayconvert the information read from barcode 80 into digital data in theform of an integer. Fiducial mark reader 29 may transmit this data tofiducial mark controller 30. At this time, fiducial mark controller 30may determine the position of the moving web based on encoded referencesignals received from encoder wheels engaged with the web. Fiducial markcontroller 30 may then transmit the position information as well as thebarcode data to analysis computer 28. Analysis computer 28 may combinethe identifier read from barcode 80 with the data representing thephysical location of the fiducial mark and store this information indatabase 32. In one embodiment, fiducial mark controller 30 communicatesdata to analysis computer 28 over a computer network using networkedsockets or other network communication protocols. Other suitable meansfor communicating data may also be used.

FIG. 7 is a diagram illustrating an example web 92 and example changesthe web may experience, including the initial introduction of anomalies,followed by the subsequent introduction of new anomalies and masking ofsome of the previous anomalies. In this example, a web is manufacturedusing three sequential manufacturing processes 90A, 90B and 90Ccorresponding, potentially, to three different production lines. Inorder to properly manufacture the web, the web may need to betransferred between multiple process lines 74 to reach the correctprocesses 90A-90C in the correct order. This transferring may includewinding the web into a roll and moving it to a different process line inthe same manufacturing plant or even shipping it to a different plant,as depicted with respect to FIGS. 1, 3.

As shown in FIG. 7, each of manufacturing processes 90A-90C mayintroduce its own anomalies into web 92. Furthermore, each manufacturingprocesses 90A-90C may change the web 92 in such a way that earlieranomalies would be difficult, if not impossible, to detect. As certainprocesses 90A-90C operate on the web, the operations (e.g., cleaning,coating, etc.) may change web 92 in a way that makes it difficult orimpossible to discover anomalies introduced by an earlier process in thefinal web. As described, each of manufacturing process 90A-90C mayinspect the web at least once, thereby collecting data regarding theanomalies detectable during each of the manufacturing processes.

Specifically, in the example of FIG. 7, a first roll (Roll # 1520098) isinitially processed in manufacturing process 90A at which time a set offiducial marks 93 to the web 92. As shown, the fiducial marks areassigned identifiers of 693-14597 and are physical “registrationmarkers” that enable electronic data to be accurately combined inmultiple unit operations containing various error sources. During thefirst manufacturing process 90A, a first set of anomalies 95A arecreated within web 92 and detected by one or more inspection systems.

Next, web 92 is cut and wound into two rolls (MR20050 and MR20051) forprocessing by a second manufacturing process 90B. In this process, web92 is unwound form the rolls and fed in the opposite direction throughmanufacturing process 90B. As shown, manufacturing process 90B hasintroduced a second set of anomalies 95B. A subset of the initialanomalies 95A is still detectable, with the remaining portion beinghidden from the inspection systems of manufacturing process 90B.

Next, web 92 is wound into two rolls (A69844 and A69843) for processingby a third manufacturing process 90C. In this process, web 92 is unwoundform the rolls and fed through manufacturing process 90B in the originaldirection used during the first manufacturing process 90A. As shown,manufacturing process 90C has introduced a third set of anomalies 95C. Asubset of the anomalies 95A, 95C are detectable, with the otheranomalies being hidden from the inspection systems of manufacturingprocess 90C.

Composite map 94 shows the local anomaly data from each of processes90A-90C once spatially registered and consolidated to form aggregateanomaly data. Composite map 94 may include registered data. Registereddata may be considered data corresponding to a common segment of webroll 7 from a plurality of processes 74, wherein the data is aligned towithin an acceptable tolerance. That is, data generated by differentprocesses 74 is correctly associated with substantially the samephysical locations on the web within the acceptable tolerance. To createthe composite map 94, conversion control system 4 may spatiallysynchronize the local anomaly data from each process 90A-90C, includingposition data for detected anomalies as well as position data for thefiducial marks 93 read during each of the processes, to a specifiedtolerance, i.e. a degree of accuracy. A high degree of accuracy may be,for example, on the order of 0-2 mm. A standard degree of accuracy maybe, for example, within 5 mm. A registration falling outside of 150 mm,or about 6 inches, may be considered “unregistered” because of a highdegree of error. As shown in FIG. 7, the exemplary composite map 94includes all of the anomalies 95 detected by the inspection systems forall processes 90A-90C.

Composite map 94 describing the combined anomalies may be used to acceptor reject an individual portion of web 92 when converting the web intofinished products. Composite map 94 may also be used to selectivelyoptimize each individual of the manufacturing process 90A-90C.

As an example, if a web consists of a printed circuit pattern, ananomaly causing a defect may be an erroneous piece of conductivematerial causing a short. In a later process, the board may be coatedwith an opaque dielectric which makes the short undetectable. Byinspecting this web once after the process of printing the conductivematerial, but before coating the web with the insulator, it may bepossible at a later time to determine that this shorted region of theweb will be defective even though it is not possible to detect theanomaly in the final form of the web due to the opaque, insulatingcoating. Another similar example may be if, rather than a short, theconductive material printer failed to print, causing a circuit to remainopen. Again, later application of an opaque dielectric would make the“open” circuit undetectable. Due to the inspection before theapplication of the dielectric, this defect may be discovered, and thedefective product removed from the pool of products to be delivered,before a delivery is ever made to a customer.

FIGS. 8A and 8B illustrate exemplary embodiments the functionaloperations and data communications performed within the networkenvironment 2 (FIG. 1) when consolidating and spatially registeringanomaly data from a plurality of different manufacturing process. InFIGS. 8A and 8B, the first manufacturing process (Unit Process #1)records data relative to its own local coordinate system. That is, ifthe first process determines that fiducial mark “7684” is at 11,367.885m, the first process will record fiducial mark “7684” at 11367.885 m.Likewise, if the first process determines that there is an anomaly aftermark “7684” at position 11,368.265 m, the first process will record ananomaly at position 11,368.265 m. Alternatively, the first manufacturingprocess may translate data to a coordinate system defined by conversioncontrol system 4. In either case, every local manufacturing process isreferenced to a pre-defined coordinate system so that all processes areautomatically spatially synchronized.

In the example of FIG. 8A, each subsequent manufacturing process Napplies its own coordinate system (coordinate system #N) when readingthe fiducial marks and the positions associated with the fiducial marksand anomalies in a manner similar to the initial process. However, theway in which this information is recorded within these subsequentmanufacturing processes record is different than the initial process.These subsequent processes record the distance of fiducial marks andanomalies by applying a transformation function to adjust the measureddistances based on the data obtained from the initial process. That is,these subsequent manufacturing processes register position data bytransforming the position data from the coordinate system N of thecurrent process N to the coordinate system of the initial process. Asinputs, an analysis computer 28 for the subsequent manufacturing processN uses position data read during the current manufacturing process N aswell as initial position data for the same fiducial marks with respectto the target coordinate system, e.g., coordinate system #1 formanufacturing process #1 in this example.

A variety of transformation functions may be used. For example, positiondata for the current manufacturing process N may be transformed withrespect to either a global offset, that is, an offset common to theentire web, or an offset calculated for each segment of a web betweentwo fiducial marks. For example, the respective analysis computer mayprocess the position data for the relevant fiducial marks and determinethat an offset of 0.004 m should be applied to position data foranomalies detected between fiducial marks “13” and “14,” but an offsetof 0.007 m should be applied to position data for anomalies detectedbetween fiducial marks “20” and “21.” Other techniques, such as linearinterpolation or application of linear scale factors, may be applied.

As another example, the first manufacturing process may record fiducialmark “61” at position 112.343 m. A subsequent manufacturing process N,however, may record the position of fiducial mark “61” at 112.356 m, anoffset of 0.013 m. The subsequent manufacturing process N, according toan embodiment as illustrated in FIG. 8A, may adjust its data accordinglyto record fiducial mark “61” as being present at location 112.343 m, andanalysis computer 28 of the subsequent unit operation may also adjustdata about anomalies detected after this fiducial mark to reflect thisoffset as well. For example, if the subsequent unit operation detects ananomaly at 112.487 m, analysis computer 28 may utilize the offset of0.013 m to adjust the position of the anomaly, recording the anomaly atposition 112.474 m.

In this manner, each manufacturing process will have produced spatiallyregistered local anomaly data that is based on a common coordinatesystem. Alternatively, as shown in the example of FIG. 8B, conversioncontrol system 4 may apply similar technique to spatially register theanomaly data. In either case, conversion control system 4 may collectthe local anomaly data and store the data as aggregate anomaly data forthe web. Conversion control system 4 then forms a composite map showingthe aggregate anomalies from the data gathered from each manufacturingprocess by, for example, utilizing an “or” function. That is, conversioncontrol system 4 may record an anomaly at a certain position on thecomposite map if any one of the processes have recorded that an anomalyis present at that position to within an acceptable degree of accuracyas described in greater detail below. Conversion control system 4 mayalso categorize the anomaly by class. The composite map can be usedlater to determine whether a certain anomaly in a certain position willcause a defect in a certain product.

FIG. 8B illustrates another embodiment in which spatial registration isperformed centrally, e.g., within conversion control system 4. In thisembodiment, each individual manufacturing process defines and referencesits own coordinate system. That is, position data gathered during eachindividual manufacturing process, such as the physical location offiducial marks and anomalies, are recorded with respect to thisindividual coordinate system. After all of the manufacturing processhave finished, or optionally as position data is received from themanufacturing processes, conversion control system 4 adjusts theposition data from all of the operations according to a commoncoordinate system and used to generate a composite map. The compositemap may comprise all previously recorded data in a single coordinatesystem.

As an example, an analysis computer 28 for a first manufacturing processmay record fiducial mark “61” at position 112.343 m in database 32. Ananalysis computer 28 associated with a subsequent manufacturing process,however, may record the position of fiducial mark “61” at 112.356 m, anoffset of 0.013 m. The subsequent manufacturing process, according to anembodiment as illustrated in FIG. 8B, may record fiducial mark “61” asbeing present at 112.356 m. At some time later, conversion controlsystem 4 may generate a composite map in which fiducial mark “61” isrecorded at 112.343 m, taking into account the 0.013 m offset, andaccordingly adjusting all position data regarding defects and anomaliesneighboring fiducial mark 61. For example, if the subsequent processrecorded an anomaly at 112.487 m, conversion control system 4 willrecord the position of the anomaly at 112.474 m according to the offsetat 0.013 m. Alternatively, a scale factor may be used, as discussed indetail below. In any case, conversion control system 4 utilizes a singlecoordinate system and translates position data for fiducial marks andanomalies to the single coordinate system when generating the compositemap. Conversion control system 4 may furthermore generate the compositemap from the data gathered from each process by utilizing an “or”function. That is, conversion control system 4 will record an anomaly ata certain position on the composite map if any one of the manufacturingprocesses have recorded that an anomaly is present at that position.

The techniques described herein may be applied to overcome a variety offactors that would prevent anomaly information from multiplemanufacturing processes from being used. For example, position datarelative to local process coordinate systems generated by externaldevices, such as rotational encoders engaged with a moving web, maydiffer from each other. However, differences in position data fromdifferent manufacturing processes are not only a result of differencesin the measurement systems, but also the result of spatial changes inthe product itself. For example, processing, winding, transportation,unwinding and reprocessing of webs may cause the webs to stretch duringthe multiple manufacturing processes.

The differences in position data between manufacturing processes cancause the position of web events, such as anomalies and defects,measured in one coordinate system to effectively “drift” relative toanother coordinate system as a web is traversed, i.e., fed through themanufacturing process. In some case, positional differences in excess of0.75% have been observed. In a system where fiducial marks are placed 2meters apart, such differences would result in a discrepancy of 14 mmbefore re-registration by a subsequent fiducial mark. That is, the“drift” caused by system differences across unit operations can resultin absolution positional errors up to 14 mm with variability rangingfrom 0 to 14 mm depending on the distance from the most recent barcode.

The techniques described herein may be applied to spatially registeranomaly information produced by web inspection systems at each of themanufacturing process. For example, one technique to correct thisuncertainty and inaccuracy is a positional correction method using alinear transformation. In one embodiment, as discussed with respect toFIG. 8A, after the initial manufacturing process, each subsequentmanufacturing process may perform linear transformations to registerposition data for detected anomalies. In another embodiment, acentralized system, such as conversion control system 4, performs thelinear transformation for all data.

In either case, one example of a linear transformation is as follows:for the first unit process, let EP_(n) be the measured position offiducial mark n and let D_(n)=EP_(n)−EP_(n−1). For the process beingadjusted, let P_(n) be the measured position of fiducial mark n and letM_(n)=P_(n)−P_(n−1). Let the scaling factor (SF) be: SF₁=1 andSF_(n)=M_(n)/D_(n) for all n>1. For an anomaly j, initially measured inposition IP_(j) between fiducial marks k and k+1, the adjusted positionAP_(j) is [(IP_(j)−EP_(k))*SF_(k+1)]+P_(k). In other words, thedistances between the fiducial marks k and k+1 as originally measuredand as measured in a subsequent process are used to form a scalingfactor SF specific to those two fiducial marks K and K+1. The distancebetween any anomaly and the fiducial mark after which the anomalyoccurred is scaled to fit the target coordinate system according to thescaling factor as described above.

Table 3 compares the difference between using a simple offset computedfor each pair of fiducial marks, and the linear transformation thatapplies a scaling factor as discussed above. In Table 3, the “distancefrom mark” measurement is the difference between the mark position andthe event position. The simple offset error is the difference betweenthe “distance from mark” measurements of the two processes. As shown inTable 3, when only using re-registration and a simple offset, positionalaccuracy can vary significantly with a maximum discrepancy of 13 mm.However, the linear transformation and application of a scaling factorhas virtually eliminated any residual error that would otherwise resultfrom application of a simple offset.

TABLE 3 Process #1 Process #N Consolidation Coordinate System CoordinateSystem Error Fid. Mark Fid. Mark Event Dist. From Fid. Mark Event Dist.From Simple Linear Label Position Position Mark Position Position MarkOffset Correction 96855 132.687 132.991 0.304 133.616 133.922 0.3060.002 0.000 96855 132.687 134.428 1.741 133.616 135.369 1.756 0.0120.000 96856 134.680 135.433 0.753 135.623 136.381 0.758 0.005 0.00096857 136.590 136.594 0.004 137.546 137.550 0.004 0.000 0.000 96857136.590 137.555 0.965 137.546 138.518 0.972 0.007 0.000 96857 136.590138.399 1.809 137.546 139.368 1.822 0.013 0.000 96858 138.641 139.8741.233 139.611 140.853 1.242 0.009 0.000 Max 0.013 0.000 Mean 0.007 0.000Min 0.000 0.000

FIG. 9 is a flowchart that provides a high-level overview of theproduction of a web. Initially, a customer, or a set of customers withsimilar needs is identified or requests a product according to certainspecifications. For example, a group of customers may request a film forglass protection; customer A may request films cut to fit automobiles,customer B may request similar films, but cut to fit home windows, andcustomer C may request films cut to fit commercial building windows.

An initial web is manufactured, e.g., at web manufacturing plant 6A, toserve as the base for the products (100). Fiducial marks may be appliedat this time to the edge of the web outside of the salable product area(102). The process of applying fiducial marks to a web is described infurther detail herein, such as with respect to FIG. 15.

The web is then collected into a web roll 7 and shipped to one ofprocess lines 74, for instance, process line 74A, at one of webmanufacturing plants 6 (104). Process line 74A then processes web roll7, during processing, the process line also collects inspection datafrom the web (106). The process line may collect inspection data one ormore times during processing. Example operation of a process line 74A,including data collection and spatial registration, is discussed infurther detail with respect to FIG. 10.

Once process line 74A is finished, the web may be sent to another ofprocess lines 74 for further processing (108). That is, if the completedprocess line was not the last process line for the web (“NO” branch of108), web roll 7 may be shipped to another process line, e.g., anotherone of processing lines 74 (110).

If, however, the completed process line is the last process line (“YES”branch of 108), the web represents a finished web roll 10 and is shippedto one of converting sites 8 (112). Conversion control system 4electronically communicates data representing a composite map of theanomaly information regarding web roll 10 to the converting site withweb roll 10. In one embodiment, conversion control system 4 creates thecomposite map from anomaly information collected from each of theprocess lines 74 which were involved in manufacturing web roll 10. Informing the composite map, conversion control system 4 may spatiallyregister the anomaly information using, for example, a lineartransformation function, discussed herein and in detail with respect toFIGS. 13, 14.

FIG. 10 is a flowchart illustrating example operation carried out by amanufacturing process line, for instance, process line 74A. Initially,process line 74A receives web roll 7 (120). In one embodiment, processline 74A may also receive data from conversion control system 4 from theprevious process line, for example, process line 74B. For example,conversion control system 4 may provide instructions as to whetherspatial registration should be performed locally, i.e., by the currentprocess line 74A or whether the conversion control system willsubsequently perform the registration. As another example, conversioncontrol system 4 may provide data necessary for the current process lineto spatially register position data to a given coordinate system, suchas the coordinate system used by the first processing line applied tothe web.

Next, the web roll is loaded and feeding of the web into the processline 74A commences. If fiducial marks are not already present on the web(122), process line 74A is configured to apply fiducial marks at anearly stage. Typically, fiducial marks should be in place before webroll 7 is applied to the first process line, though there may beinstances where fiducial marks are corrupted and need to be replaced. Inaddition, a process line may be configured to apply additional fiducialmarks to a web already having fiducial marks if additional informationneed be provided. The application of fiducial marks is discussed ingreater detail with respect to FIGS. 13, 14.

As the web moves through the process line, the inspection systems ofprocess line 74A acquire information regarding fiducial marks andanomalies using fiducial mark reader 29 and image acquisition devices26A-26N (“image acquisition devices 26”). That is, the inspectionsystems will begin inspecting the web for anomalies. Although theprocess of collecting data is continuous (that is, the web may beconstantly moving), the data collection process is described withrespect to discrete segments of a web between fiducial marks for thepurpose of clarity.

Analysis computer 28 detects and records anomalies with respect to themost proximate fiducial marks. Specifically, analysis computer 28locates fiducial marks using fiducial mark reader 29 (126). That is,fiducial mark controller 30 acquires identifying information about thefiducial marks from fiducial mark reader 29 and transmits theinformation to analysis computer 28. Analysis computer 28, in turn,records this identifying information, along with the position of thefiducial mark on the web, in database 32 (127).

During this process, image acquisition devices 26 scan the web toproduce image data useful for detecting anomalies (128). When one ofimage acquisition devices 26, for example, image acquisition device 26A,discovers an anomaly, the respective acquisition computer, for example,acquisition computer 27A, will inform analysis computer 28 of thepresence and position of the anomaly. Analysis computer 28 will recordthe most recent fiducial mark, the position of the anomaly, and thedistance from the fiducial mark to the anomaly in database 32 (130). Inone embodiment, analysis computer 28 will adjust positional data withrespect to positional data received from conversion control system 4 inorder to maintain a single coordinate system for creating a compositemap. In another embodiment, analysis computer 28 will utilize thecoordinate system local to process 74A and conversion control system 4will spatially register the anomaly information and form the compositemap after all process lines 74 have finished processing the web, asdiscussed with respect to FIG. 11.

If the end of the web has not been reached (“NO” branch of 134),analysis of web roll 7 will continue as above with respect to thisfiducial mark and anomalies occurring after this fiducial mark. If theend of the web has been reached, however, (“YES” branch of 134),analysis computer 28 extracts the data about anomalies in web roll 7,gathered during this inspection, from database 32 and transmit the datato conversion control system 4 (136).

FIG. 11 is a flowchart illustrating central reconciliation of datagathered from a plurality of processes in one exemplary embodiment. Inthis example, it is assumed that all of process lines 74 have utilizedlocal coordinate systems to collect data without registration. As aresult, conversion control system 4 spatially registers the localanomaly information to conform to a single coordinate system. Thisembodiment may reduce the overhead of each of process lines 74 whilecollecting information from the inspection of web roll 7.

Either during the multi-process line production or after all processlines 74 have finished processing web roll 7 to produce finished webroll 10, conversion control system 4 receives the local anomalyinformation produced by each of process lines 74 (140). As discussedbelow, conversion control system 4 analyzes and converts the localanomaly information from each of process lines 74 one-by-one to registerthe position data to a common coordinate system. After conversioncontrol system 4 has retrieved all of the data, it aligns the data to acomposite map of the web which has its own coordinate system, which maymatch one or more of the coordinate systems of process lines 74.

Conversion control system 4 begins by retrieving the local anomalyinformation generated by the first process line, for example, processline 74A (142). This first process line may or may not be the firstprocess line to have performed processing of web roll 7. Conversioncontrol system 4 then spatially registers the local anomaly informationfrom process line 74A to a target coordinate system, which may be atarget coordinate system defined by conversion control system 4 or maybe a coordinate system used by one of the other manufacturing processes(144). That is, conversion control system 4 processes each anomaly dataentry and adjusts the position data using a translation functiondetermined based on use of the fiducial marks within each of the processlines. In one embodiment, the retrieved data may look similar to thatdepicted in Table 3.

Next, conversion control system 4 retrieves the anomaly information foranother one of the process lines, for instance process line 74B, used bythe web (146). Process line 74B may or may not be the process lineimmediately subsequent to process line 74A; process line 74B may haveprocessed web roll 7 before process line 74A, immediately after processline 74A, or after another of process lines 74. After retrieving theanomaly information from process line 74B, conversion control system 4spatially registers the anomaly information in a similar manner (148).The spatial registration adjusts the positions of the anomalyinformation so as to compensate for a variety of factors, including thatthe web may have been trimmed or combined with another web, or may havestretched during processing, which may cause the positions of fiducialmarks, and likewise anomalies, to vary from the positions recorded byother processes.

Once finished with all of the data from process line 74B, conversioncontrol system 4 determines whether any local anomaly information forthe web remains unregistered (150). If there is more anomaly informationto be registered (“YES” branch of 150), then conversion control system 4will retrieve the local anomaly information for the process (146) andspatially register the data as discussed above (148). If no moreunregistered anomaly information remains, however, (“NO” branch of 150),conversion control system 4 generates a composite map based on thespatial registered anomaly information, determines a converting plan forthe web roll, and sends the composite map to the converting site, forexample, converting site 8A, along with the finished web roll 10. Thusconverting site 8A may convert finished web roll 10 into product 12Aaccording to the data in the composite defect map and the conversionplan.

FIG. 12 is a flowchart illustrating operations for performing a lineartransformation on position data collected from two different processlines, for example, process lines 74A and 74B. Although discussed withrespect to conversion control system 4, analysis computer 28 may alsoperform linear transformation of data collected by the current processline. Conversion control system 4 may need to perform a lineartransformation on the data collected from multiple process lines for anumber of reasons, including because the web may have stretched duringprocessing or for other reasons.

Generally, a linear transformation is used to map data from onecoordinate system onto a different coordinate system. In this example,anomaly positions are linearly transformed from the coordinate system ofprocess line 74B to fit the coordinate system of process line 74A. Adistinct linear transformation may be performed for each portion of webbetween two fiducial marks.

First, conversion control system 4 retrieves relevant data from bothprocess lines 74A and 74B (160). Conversion control system 4 ensuresthat the data from both process lines 74A and 74B is considered asoriented along the web in the same direction. This may be necessary dueto the nature of a web roll being wound and unwound, i.e., that a givenprocess may begin with either end of the web depending on where theprocess falls within the recipe. Conversion control system 4 maydetermine the direction of the data according to the fiducial mark data.If the data direction does not match, conversion control system 4 maylogically reverse the direction of one of the two process lines suchthat the data direction matches. In one example, conversion controlsystem 4 reverses the direction by offsetting the data from the end ofthe web roll rather than the beginning as may occur in the forward case.

After ensuring that the data is flowing in the same direction for eachprocess, conversion control system 4 processes the data to locate afirst fiducial mark in common between process lines 74A and 74B (162).Conversion control system 4 records the position of this mark asrecorded by process line 74A (164). Conversion control system 4 thenlocates the position of the next fiducial mark as recorded by processline 74A (166). Conversion control system 4 records the differencebetween the two fiducial marks as D_(n) (168). Next, conversion controlsystem 4 finds the position of the next fiducial mark within the datarecorded by process line 74B (170) and records the difference betweenthe mark and the previous mark within the data of process line 74B asthe difference M_(n) (172).

Conversion control system 4 uses the differences D_(n) and M_(n) tocreate a scaling factor SF_(n) for each data point between the twofiducial marks, such that SF_(n)=M_(n)/D_(n) (174). Conversion controlsystem 4 then processes the local anomaly information for process 74B todetermine locate any anomaly positions which need to be scaled (175).For each anomaly data point recorded by process line 74B, conversioncontrol system 4 uses the scaling factor SF_(n) to transform each datapoint into the coordinate system or process line 74A. To do so, thedistance from the fiducial mark to the anomaly is recorded as IP_(j).This distance is “scaled” by determining SD_(j)=IP_(j)*SF_(n). Then, tolocate the new, adjusted position AP_(j) on the common coordinatesystem, conversion control system 4 adds the scaled distance SD_(j) tothe position of the fiducial mark as recorded by process line 74B (176).Conversion control system 4 adjusts the position of each anomaly betweenthese two fiducial marks in this manner (178). Conversion control system4 then finds the next fiducial mark in common and repeats the processuntil no more fiducial marks are in common (179).

Once all anomalies between these two fiducial marks have been adjusted,conversion control system 4 determines whether it has reached the end ofthe data gathered for either process lines 74A and 74B (180). If bothhave more data to analyze (“NO” branch of 180), conversion controlsystem 4 will find the position of the next fiducial mark as recorded byeach of process lines 74A and 74B and transform the anomaly dataaccording to the method above. However, if the end of the data has beenreached for either process line (which may be possible due to a webbeing split, combined with another web, or for other reasons) (“YES”branch of 180), conversion control system 4 has finished linearlytransforming this set of data, so conversion control system 4 continueswith other processing, either linearly transforming a new pair ofprocess lines 74, combining data from process lines 74, or transmittingdata to one of converting sites 8.

In some embodiments, a modeling engineer may generate one or moremathematical models for the manufacturing operations performed on theweb throughout the plurality of manufacturing process lines. Duringoperation, data from the mathematical models may be used to spatiallyregister the position data for the different manufacturing processlines. For example, a linear or nonlinear transformation may be appliedto spatially register the position data for each of the anomalies,wherein the transformation is calculated using previously generatedmathematical models of the web process for the web region of interest.

FIG. 13 is a block diagram illustrating an exemplary embodiment offiducial mark writer 181. In some embodiments, process line 74A mayinclude fiducial mark writer 181 for application of original orsupplemental fiducial marks. In the exemplary embodiment of fiducialmark writer 181, fiducial mark writer 181 comprises encoder 186, reader188, writer 190, and trigger module 192. Fiducial mark writer 181 istypically positioned such that writer 190 is near the edge of web 20such that fiducial marks will be written outside of the salable area ofweb 20. Fiducial mark writer 181 can write an initial set of fiducialmarks to a web which has no fiducial marks. Fiducial mark writer 181 canalso rewrite fiducial marks to a web which has one or more corruptedfiducial marks. Fiducial mark writer 181 can also interlace a new set offiducial marks between an existing set of fiducial marks. That is,fiducial mark writer 181 is able to apply a new set of fiducial marks toweb 20 such that the new set does not corrupt the existing set. Fiducialmark writer 181 may utilize either of the exemplary fiducial markembodiments depicted in FIGS. 5A-5B, or fiducial mark writer 181 can bemodified to write a different embodiment of a fiducial mark.

FIG. 13 depicts web 20 with a set of existing fiducial marks 182A-182N(“existing fiducial marks 182”). That is, existing fiducial marks 182were applied to web 20 at some earlier stage of the development of web20. FIG. 13 depicts fiducial mark writer 181 as interlacing a set of newfiducial marks 184A-184B (“new fiducial marks 184”) between existingfiducial marks 182. However, fiducial mark writer 181 is capable ofapplying a set of new fiducial marks 184 to web 20 without a set ofexisting fiducial marks 182. The determination of where to write a newfiducial mark is discussed in greater detail with respect to FIG. 15.

In an exemplary embodiment, encoder 186 comprises a wheel pressed firmlyagainst the surface of web 20. Encoder 186 may transmit an encoder pulsefor each partial revolution of the wheel to trigger module 192. Triggermodule 192 can measure the distance along web 20 according the number ofencoder pulses and the circumference of the wheel of encoder 186. Forexample, if the wheel is ten centimeters in circumference and encoder186 gives an encoder pulse every hundredth of a rotation, then afterfifty encoder pulses trigger module 192 can determine that the web hastraveled five centimeters. In this way, trigger module 192 can measurethe distance web 20 has traveled extremely accurately.

Reader 188 may be very similar to the fiducial mark reader as depictedin FIG. 6. In the exemplary embodiment of fiducial mark writer 181,reader 188 reads existing fiducial marks 182 and communicatesinformation read from existing fiducial marks 182 to trigger module 192.Trigger module 192, utilizing distance information obtained from encoder186, can thus determine the position of existing fiducial marks 182 to ahigh degree of accuracy by being configured with the distance betweenencoder 186 and reader 188.

Trigger module 192 may instruct printer 190 to write a new fiducialmark, for example, new fiducial mark 184A, onto the surface of web 20.Reader 188 may read the newly applied fiducial mark, e.g. new fiducialmark 184A, once new fiducial mark 184A passes under reader 188. Triggermodule 192 may record position information about newly written fiducialmarks 184 as well. In one embodiment, printer 190 comprises an inkjetprinter. Printer 190 may comprise any device capable of applying afiducial mark to web 20. For example, printer 190 may comprise a laserprinter or a device to secure mechanical or magnetic labels to web 20.

FIGS. 14A-14D are block diagrams illustrating the positions of existingand inserted fiducial marks. FIG. 14A illustrates an example wherein newfiducial marks 184 are interlaced between a full set of existingfiducial marks 182. In this example, web 20 has a set of existingfiducial marks 182. As shown in FIG. 14A, fiducial marks may be near theedge of the web outside of salable area 199 (bounded by a verticaldashed line running parallel to the web and fiducial marks in FIG. 14).Each of existing marks 182, for example existing mark 182A and existingmark 182B, may be spaced at approximately the same distance apart. Inone embodiment, this distance may be about 2 meters. New fiducial marksmay be inserted, for example, in accordance with the method describedwith respect to FIG. 15. Suitable fiducial marks for either or both ofnew fiducial marks 184 and existing fiducial marks 182 are depicted inFIG. 5.

FIG. 14B illustrates an example wherein web 20 has a set of existingfiducial marks 182, but the existing set begins late. That is, there isa space on web 20 from the start of the web to some point 194 down web20 where the space does not have existing fiducial marks, but from point194 on, web 20 does have existing fiducial marks 182. FIG. 14B depictsthese void spaces as void spaces 185. In one embodiment, for example asdiscussed with respect to FIG. 15, fiducial mark writer 181 may applynew fiducial marks 184 to spaces between existing fiducial marks 182. Inanother embodiment fiducial mark writer 181 may in addition apply newfiducial marks to void spaces 185 where fiducial marks should have beenpresent. One may operate fiducial mark writer 181 so as to fill voidspaces 185 with marks in a distinct format from new fiducial marks 184.That is, fiducial mark writer 181 may utilize a different format offiducial marks for filling void spaces 185 than the format used foradding new fiducial marks 184. The format of the fiducial marks forfilling void spaces 185 may be calibrated so as to match the format ofexisting fiducial marks 182. In another embodiment, the format of thefiducial marks for filling void spaces 185 may be the same as the formatof new fiducial marks 182. In yet another embodiment, fiducial markwriter 181 will not write any fiducial marks in void spaces 185 but onlyapply new fiducial marks 184.

FIG. 14C illustrates an example wherein web 20 has a set of existingfiducial marks 182, but the existing set ends early. That is, there is aspace on web 20 from the start of the web to some point 196 down web 20where the space has existing fiducial marks 182, but from point 196 on,there are no existing fiducial marks. FIG. 14C depicts these void spacesas void spaces 185. In one embodiment, for example as discussed withrespect to FIG. 15, fiducial mark writer 181 may apply new fiducialmarks 184 to spaces between existing fiducial marks 182, and in additionfiducial mark writer 181 may also apply new fiducial marks to voidspaces 185 where fiducial marks should have been present. Again, theformat of the fiducial marks for filling void spaces 185 may match theformat of existing marks 182 or the fiducial marks for filling voidspaces 185 may match new fiducial marks 184.

FIG. 14D illustrates an example wherein web 20 has a set of existingfiducial marks 182, but there is a gap in the set. That is, there is aspace on web 20 between two points 197, 198, wherein web 20 has existingfiducial marks 182 from the start of the web to point 197, and frompoint 198 to the end of the web, web 20 has existing fiducial marks 182,yet between points 197 and 198, web 20 has void spaces rather thanexisting fiducial marks. FIG. 14D depicts these void spaces as voidspaces 185. In one embodiment, for example as discussed with respect toFIG. 15, fiducial mark writer 181 may apply new fiducial marks 184 tospaces between existing fiducial marks 182, and in addition fiducialmark writer 181 may also apply new fiducial marks to void spaces 185where fiducial marks should have been present. Again, the format of thefiducial marks for filling void spaces 185 may match the format ofexisting marks 182 or the fiducial marks for filling void spaces 185 maymatch new fiducial marks 184.

FIG. 15 is a flowchart illustrating exemplary operations involved inapplication of fiducial marks to a web. FIG. 15 depicts an examplemethod by which fiducial mark writer 181 may apply new, interlacedfiducial marks to a web as an example method by which fiducial markwriter 181 may apply fiducial marks to void spaces, that is, spaceswhere fiducial marks should exist but do not. FIG. 15 also depicts anexample method by which fiducial mark writer 181 may apply a set offiducial marks to a web which has no existing fiducial marks.

First, web 20 must be in place and ready for marking (FIG. 2). At thistime, fiducial mark writer 181 is activated (200). Concurrently, web 20is wound off of a supporting roll and collected onto supporting roll,causing web 20 to travel past fiducial mark writer 181. In response todetecting a leading edge of the web, trigger module 192 initializes adistance counter D to zero (202). In addition, trigger module 192initializes a global variable FD to represent the fixed distance betweenfiducial marks. In one embodiment, trigger module 192 presumes that FDis two meters unless it receives contrary instructions from an operator.Trigger module 192 also maintains a position offset variable PO whichrepresents the distance between reader 188 and printer 190.

When web 20 has existing fiducial marks 182, reader 188 sends a fiducialpulse to trigger module 192 when reader 188 detects a fiducial mark. Aslong as a fiducial pulse has not occurred (“NO” branch of 204), thefiducial writer will continue to wait for a fiducial pulse. Once afiducial pulse occurs (“YES” branch of 204), trigger module 192 willinitialize a new counter N to zero and distance D to zero (206). Triggermodule 192 will then enable the new counter N (208), then wait for anencoder pulse from encoder 186 (210), and will continue to wait as longas an encoder pulse has yet not occurred (“NO” branch of 210).

Once an encoder pulse occurs (“YES” branch of 210), however, thefiducial writer will increment N by 1 (that is, N=N+1) (212). Triggermodule 192 will then determine whether N is equal to FD/2 (214); if not(“NO” branch of 214), trigger module 192 will wait for a new encoderpulse. If N is equal to FD/2, however, (“YES” branch of 214), trigger192 will disable the “new” counter (216) and instruct printer 188 toprint a new fiducial mark, for example, new fiducial mark 184B (218). Inother words, trigger module 192 will instruct printer 188 to print a newfiducial mark half-way between existing fiducial marks. Trigger module192 will then prepare to print the next fiducial mark by disabling thefiducial present sensor input gate for a distance of (3*PO)/2 (228).Those skilled in the art are capable of modifying the above instructionsto print new fiducial marks 184 at other intervals and positions; forexample, one could modify the above instructions to print new fiducialmarks 184 at one-quarter of the distance between existing fiducial marks182.

When web 20 has existing fiducial marks 182 but is missing a certainmark, for instance, if existing fiducial mark 182C has been corrupted,fiducial mark writer 181 can replace fiducial mark 182C. Trigger module192 will be expecting a fiducial pulse but will not receive one, asfiducial mark 182C is corrupted. Therefore, trigger module 192 will usedistance counter D to measure a distance FD from the previous fiducialpulse to where existing fiducial mark 182C should be located (220, 222,224). At this point, trigger module 192 will instruct writer 190 towrite fiducial mark in the correct position (226). Trigger module 192will again prepare to print the next fiducial mark by disabling thefiducial present sensor input gate for a distance of (3*PO)/2 (228).

When web 20 has no existing fiducial marks 182, or when web 20 has a setof existing fiducial marks 182 as well as void spaces 185 as shown inFIG. 14, fiducial mark writer 181 may determine when to print a newfiducial mark in a slightly different way. Initially, trigger module 192will initialize distance counter D to zero (202). As there are noexisting fiducial marks 182, trigger module 192 will never receive afiducial pulse (“NO” branch of 204), so trigger module 192 will wait foran encoder pulse from encoder 186 (220). Once an encoder pulse occurs(“YES” branch of 220), trigger module 192 will increment D (that is,D=D+1) (222). Trigger module 192 will then determine whether D is equalto FD (224). If not (“NO” branch of 224), trigger module 192 will waitfor a new encoder pulse and continue to increment D. If D is equal toFD, however (“YES” branch of 224), trigger module 192 will instructprinter 188 to print a new fiducial mark, for example, new fiducial mark184B (226). Trigger module 192 will then reinitialize D to zero (202)and start over again. In other words, when no fiducial marks arepresent, printer 188 will print new fiducial marks 184 at a distance ofFD apart. In one embodiment, fiducial mark writer 181 prints newfiducial marks 184 at a distance of 2 m apart.

Using this exemplary method, fiducial mark writer 181 is able to eitherwrite new fiducial marks 184 to a web without requiring any existingfiducial marks 182, interlace new fiducial marks 184 between existingfiducial marks 182, or even replace a missing fiducial mark.

FIG. 16 is a flowchart illustrating exemplary operations involved inidentifying areas of web material for a given web roll segment that havebeen processed throughout multiple manufacturing operations and,therefore, are candidates for spatially synchronization. In oneembodiment, conversion control system 4 uses fiducial marks to identifyvarious web rolls 7, 10. That is, conversion control system 4 mayidentify segments of a particular web roll 7, 10 that have been throughcommon manufacturing processes 74 by identifying overlapping fiducialmarks from each of the processes that are present on the web roll. Inone embodiment of conversion control system 4 uses the example methoddepicted in FIG. 16 to create a correspondence between data collectedfrom a sequence of the various processes 74 for a particular web rollsegment.

First, a particular web roll 10 of interest is selected (350). Typicallya user may select a web roll 10 or a portion thereof through a graphicaluser interface (“GUI”) presented by conversion control system 4.However, in other embodiments, other devices may interface withconversion control system 4 to automatically or semi-automaticallyselect a web roll and retrieve data from conversion control system 4.Conversion control system 4 may also permit accessing data collected forunfinished web rolls 7 and in-process web rolls, in addition to finalweb rolls 10.

Once conversion control system 4 has a particular web roll for which togather data, conversion control system 4 may begin to search for datagathered by the various processes 74 and collected by consolidationservers 76. Conversion control system 4 then identifies a complete setof possible predecessor processes 74 that may be associated with the webroll (352). For example, conversion control system 4 may identify a mostrecent processes 74 for a particular web roll and then recursivelyidentify possible predecessor processes 74 to create a tree-like logicalconstruct representing the potential processing history of the web roll.In one embodiment, conversion control system 4 may exhaustively searchfor data corresponding to the web roll. In another embodiment,conversion control system 4 may use the method described with respect toFIG. 17 below to reduce the search space in which conversion controlsystem 4 will query for data corresponding to the selected web roll.

After conversion control system 4 has assembled the search space fordata associated with the web roll, conversion control system 4 maysearch for data associated with the web roll (354). Specifically,conversion control system 4 may search for fiducial marks that matchfiducial marks of the web roll of interest within the data from each ofthe processes in the search space. Conversion control system 4 may alsosearch an entire roll for specific segments of the roll, defined by acertain range of fiducial marks (for example, segments 376A, 376B, FIG.18B). Conversion control system 4 may search for overlapping fiducialmarks among the processes that form a segment of the web roll (356). Inone embodiment, if conversion control system 4 cannot determine whetheroverlap exists (“?” branch of 356), e.g. due to gaps in the data of theweb roll data being triggered (see Table 5, below), then conversioncontrol system 4 will exhaustively search all of the data (360) to lookfor overlap (362), rather than using an optimized method, such as themethod discussed with respect to FIG. 17. If no overlap exists for aparticular segment (“NO” branch of 356 or “NO” branch of 362),conversion control system 4 will search for the next web roll segment.If overlap does exist (“YES” branch of 362 or “YES” branch of 356),conversion control system 4 will record data associated with the rollwith the overlapping fiducial marks data from a predecessor roll for theweb roll of interest selected at step 350.

Conversion control system 4 continues to search if more roll segmentsexist on the web roll of interest (364). Once conversion control system4 has finished searching all of the roll segments associated with aparticular process, conversion control system 4 will select a nextprocess based on the process list generated at step 354 (366).

After conversion control system 4 has gathered the data, conversioncontrol system 4 may analyze the data (368). Conversion control system 4may search for and identify all segments of web with overlappingfiducial marks for each process. Briefly referring to the example ofFIG. 18A, conversion control system 4 may identify roll segments 376Aand 376B, each of which contain web roll segments common to the threeprocesses Process A, Process B, and Process C. Conversion control system4 may then adjust the data according to the segments and align the datasuch that the data is usable for analysis to, for example, markanomalies and/or defects on the surface of the web, or to analyze theprocesses to determine where anomalies or defects are being introducedso that the processes may be adjusted or corrected.

FIG. 17 is a flowchart illustrating exemplary operations involved indetermining the search space of data associated with particular webrolls 7, 10. In one embodiment, conversion control system 4 uses theexample method depicted in FIG. 17 to improve the performance oflocating potential processes 74 that may have collected data associatedwith particular web rolls 10. Conversion control system 4 may, forexample, use product and process constraints that occur due to thenature of the constraints to reduce the search space when retrievingdata relevant to a particular web roll 10.

As an example, film making operations cannot have predecessor rolloperations. To make use of these process constraints, one may assemble a“process association map” that describes the possible interactionsbetween various manufacturing processes 74. The process association mapmay describe, for example, possible predecessor processes for each ofprocesses 74. Table 4 shows an example process association map.

TABLE 4 Plant A Plant B Plant C Plant D Process A1 Process B1 Process C1Process D1 Possible Predecessors: Possible Predecessors: PossiblePredecessors: Possible Predecessors: none Plant A, Process A1 Plant A,Process A2 none Plant A, Process A2 Plant A, Process A5 Plant A, ProcessA3 Plant D, Process D1 Plant A, Process A4 Plant C, Process C1 Plant D,Process D2 Process A2 Process B2 Process D2 Possible Predecessors:Possible Predecessors: Possible Predecessors: none Plant A, Process A1Plant A, Process A2 Plant A, Process A2 Plant A, Process A3 Plant A,Process A3 Plant B, Process B3 Plant A, Process A4 Plant C, Process C1Plant C, Process C1 Plant D, Process D2 Process A3 Process B3 PossiblePredecessors: Possible Predecessors: none Plant A, Process A5 Plant C,Process C1 Process A4 Possible Predecessors: none Process A5 PossiblePredecessors: none

Conversion control system 4 may first select a particular web roll ofinterest (300), i.e., a web roll for which conversion control system 4requires data. Conversion control system 4 then determines the last oneof processes 74 to have performed operations on the web roll (302).Next, conversion control system 4 adds the process to a hierarchicallyarranged set of nodes that may form a tree structure, wherein the lastprocess may occupy the root of the tree (304). Conversion control system4 may then determine whether the last process has any predecessorprocesses (306). If not (“NO” branch of 306), then there is no reason tocontinue, because no more data could exist for the web roll, as noprocess could have preceded the most recently analyzed process.

If there could have been a predecessor process, however, (“YES” branchof 306), conversion control system 4 selects one of the predecessorprocesses (308). Conversion control system 4 may then essentiallyperform a recursive instance of the method portrayed in FIG. 17, exceptthat in the recursive instance the web roll of interest is alreadyselected. That is, conversion control system 4 may determine whetherthis predecessor process itself has any predecessors and add them to thetree as one branch from the root (310).

Next, conversion control system 4 determines whether any morepredecessor processes exist for the currently selected process (312). Ifnot (“NO branch of 312) then the method may end. However, if there aremore possible predecessor processes (“YES” branch of 312), conversioncontrol system 4 will perform the recursion for each of the predecessorprocesses for the current process and add each of them as respectivebranches to the root of the tree.

Table 5 below presents an example data set for multiple processes thatconversion control system 4 may analyze and optionally present to auser. Table 5 includes fields Roll Name, First, Last, Min, Max Expected#, Actual #, and Comments. Roll Name is the name of a roll segment for aparticular process. First is the fiducial mark with smallest associateddistance in the local coordinate system of the process. Last is thefiducial mark with largest associated distance in the local coordinatesystem of the process. Min is the fiducial mark with the smallest value.Max is the fiducial mark with the largest value. Expected # is theexpected number of fiducial marks of the roll, equal to (Max−Min+1).Actual # is the actual number of fiducial marks on the roll asdetermined by the process or process inspection system. The commentsfield describes aspects or status information of the roll, such aspotential flaws such as data gaps.

TABLE 5 Fiducial Marks Roll Name First Last Min Max Expected # Actual #Comments AIS1-0001 1 144 1 144 144 144 forward AIS1-0002 192 490 192 490299 299 forward AIS2-0001 762 952 762 952 191 191 forward AIS3-0011 12101400 1210 1400 191 191 forward BIS2-0007 143 86 86 143 58 58 reverseBIS2-0008 81 1 1 81 81 81 reverse BIS2-0009 475 350 350 475 126 126reverse BIS2-0010 333 163 163 333 171 171 reverse BIS2-0011 155 32 32155 124 124 reverse BIS3-0125 1400 1222 1222 1400 179 179 reverseCIS1-0003 88 140 88 140 53 53 forward CIS2-0001 6 472 6 472 467 197forward, data gap CIS3-0001 164 155 33 321 289 280 discontinuity, datagap? CIS3-0002 951 779 779 951 173 173 reverse CIS3-0003 1225 1391 12251391 167 167 forward

To determine whether data for the selected process is associated withthe particular web roll, conversion control system 4 uses the fiducialmarks of the web roll. If a successor process has not or cannot join tworolls, conversion control system 4 may look for overlap in the fiducialmarks between the two processes. If a successor process may have joinedtwo rolls, conversion control system 4 compares the expected fiducialmark and the actual fiducial mark. If the expected fiducial mark countand the actual fiducial mark count differ by a certain percent,conversion control system 4 may determine that a gap exists in the dataand will exhaustively search the data. In one embodiment, the certainpercent is a five percent difference in the expected fiducial mark count(“Expected #”) and the actual fiducial mark count (“Actual #”). Ifconversion control system 4 determines that First is not equal to eitherMin or Max, or Last is not equal to either Min or Max, conversioncontrol system 4 may also determine that a data gap exists and willexhaustively search the data. Otherwise, conversion control system 4 mayproceed with the optimized search method described herein.

As one example operation of the method, the processes may conform to thehierarchy portrayed in Table 4 above. The last process for a particularweb roll may have been process D1 of plant D. In this case, conversioncontrol system 4 will gather all data from process D1 and end, as thereare no possible predecessors for process D1.

As another example operation of the method, the processes may againconform to the hierarchy portrayed in Table 4 above. The last processfor a particular web roll may have been process D2 of plant D. In thiscase, conversion control system 4 will gather all data from process D2.Conversion control system 4 will then gather data from each of processesA2 and A3 of plant A related to the web roll. Conversion control system4 may then gather data from process B3 of plant B. Process B3 itself haspossible predecessors A5 and C1, so conversion control system 4 willgather data from processes A5 and C1. Process C1 has predecessorprocesses A2, A5, and D1. Thus, conversion control system 4 will gatherdata related to the web roll from each of A2, A5, and D1, none of whichhas any predecessor processes. Next, conversion control system 4 willgather data from process C1 again, as process C1 is a predecessor to D2(as well as to process B3). Therefore, conversion control system 4 willgather data from processes A2, A5, and D1.

This method may provide several advantages. For example, the method mayreduce the time required to search for data related to a particular webroll or web roll segment. The operating time of searching for datarelated to a web roll according to the example embodiment of theimproved method described with respect to FIG. 17 may be a logarithmicfunction of the number of processes 74 present in the system, as opposedto being directly related to the number of processes 74 present in thesystem. That is, conversion control system 4 may create a depth-firstsearch tree from the processes for which a significant number ofbranches may be pruned to perform the search, as opposed to exhaustivelysearching for data. Those skilled in the art will recognize that, forthe timing functions Big-Theta (Θ), which describes both upper and lowerbounds, Big-Oh (O), which describes upper bounds, and Big-Omega (Ω),which describes lower bounds, this method may change the run time fromΘ(n*m), where “n” is the number of processes and “m” is the maximumamount of data stored for any one process, to O(n*m) and Ω(m*log(n)).

FIG. 18A is a block diagram illustrating an example web roll in variousstages of manufacturing, wherein the web roll has been split and joinedin subsequent processes. Initially, a manufacturing process, i.e.Process A, has processed web roll 370. In a subsequent process, web roll370 has been split into two web roll segments 372A, 372B, each of whichhave been processed by a different process, i.e. Process B. Later, webroll segments 372A, 372B have been joined with another web roll segmentto form web roll segment 374 which a third process, i.e. Process C, hasmanufactured. During each process in the evolution of the web roll,certain fiducial marks 376A, 376B identify a segment of the web rollthat has undergone the same sequence of manufacturing. In this example,there are two segments of web that have undergone the same sequence ofmanufacturing: segment 376A, comprising fiducial marks 4878 to 4885, andsegment 376B, comprising fiducial marks 4889 to 4897.

FIG. 18B is a block diagram showing exemplary segments 376A, 376B ofFIG. 18A. FIG. 18B depicts how conversion control system 4 may realignthe segments to analyze the data from each segment. Each of segments376A, 376B have passed through the same series of manufacturingprocesses, i.e. Process A, Process B, and Process C. Conversion controlsystem 4 may use the methods discussed with respect to FIGS. 16 and 17to determine segments 376A, 376B and to align the segments so thatconversion control system 4 may extract data regarding these segmentsfrom the pool of data from the processes that may have operated onsegments 376A and 376B. Conversion control system 4 may then extractdata common to segments 376A, 376B to mark positions of anomalies ordefects on the surface of the web as described in detail in co-pendingapplication Floeder et al., U.S. App. No. 2005/0232475, Apparatus andMethod for the Automated Marking of Defects on Webs of Material (filedApr. 19, 2004, published 2005), which is incorporated by reference inits entirety. Conversion control system 4 may use the data in other waysas well, for example, to optimize the processes or to repair or performmaintenance on the processes to reduce the number of anomalies and/ordefects occurring in the web as a result of the processes.

FIG. 19 is a screenshot illustrating a comparison of data gathered fromtwo process lines, for instance, process lines 74A and 74B. In oneembodiment, conversion control system 4 comprises a graphical userinterface system allowing a user to interact with conversion controlsystem 4. As an example, the graphical user interface may allow a userto observe and compare data collected from a roll from multipleprocesses. FIG. 19 depicts graphical user interface (“GUI”) 250. GUI 250comprises web ID text box 252, process A text box 254, process B textbox 256, submit button 258, and result pane 260.

Conversion control system 4 may present GUI 250 to a user upon a userrequest to compare data from process lines 74. A user may wish to viewthis data to optimize a particular process line, for instance, processline 74A. Once conversion control system 4 has received a request toshow comparison data, conversion control system 4 will present the userwith GUI 250. A user may then enter the numerical identification (“ID”)of a particular web roll 10 in web ID text box 252. In one embodiment,web rolls might only be identified by fiducial marks; in that case, webID text box 252 may be modified by those skilled in the art to retrievedata associated with a particular fiducial mark or a range of fiducialmarks. An ID for a web or a process may be numerical, alphabetical, oralphanumerical. In some embodiments, web ID text box 252 may comprise adrop-down text box or may provide a search function; for instance, auser may search for the ID of a particular web roll based on where theweb was manufactured, what process lines the web underwent, what typesof products 12 the web was eventually converted into, to which ofconverting sites 8 the web was delivered, or other properties of a web.

A user may also enter the ID of the desired process lines 74 to becompared in text boxes 254, 256. Likewise, in other embodiments textboxes 254, 256 may comprise drop-down boxes or provide searchfunctionality to search for the ID of a particular process line based onwhich of manufacturing plants 6 the process line is located, whether theprocess line comprises a fiducial mark writer 181, what type of web(e.g. paper, woven, metal, film, etc.) the process line operates upon,or other features of a process line.

Once a user has entered the information in text boxes 252, 254, 256, theuser may then select submit button 258. Submit button 258 triggersconversion control system 4 to retrieve data in text boxes 252, 254,256. Conversion control system 4 then retrieves data regarding thedesired processes with respect to the web ID in accordance with theinformation entered into text boxes 252, 254, 256. Conversion controlsystem 4 then displays the requested and retrieved information in resultpane 260. If an error occurs during retrieval, for example if conversioncontrol system 4 has no information about any web with ID matching thatof the requested web ID, conversion control system 4 may instead displayan error message in result pane 260 informing the user as to the natureof the error, for example, “ERROR: Web ID not found.” In otherembodiments, error messages may appear in other forms, for example in anew window or text box.

In the example of FIG. 19, exemplary GUI 250 is shown in response toinput by which a user has requested data regarding a web with ID“96800.” In addition, the user has also requested to compare datacollected from process lines “1” and “4”. Having pressed submit button260, conversion control system 4 has retrieved and displayed data aboutweb “96800” from process lines “1” and “4” in result pane 260. Thus, theuser is able to view and compare the data gathered from these processlines to make determinations about the web and possibly to makedecisions about how the process lines could be altered to improveproduct yield and decrease defects.

FIG. 20 depicts an example of an alternative embodiment for applyingtechniques to spatially synchronize position data, such as position datafor attributes or anomalies, for a plurality of different stages withina single process line. FIG. 20 depicts system 400 that includes a singleprocess line 402 and analysis computer 408. Process line 402 includesmultiple operations 404A-404N (“operations 404”) performed within aplurality of distinct stages 405A-405N. As described below, variousoperations 404 may be applied within each of the different stages 405,and each of the stages may use different coordinate systems and/orfiducial marks in order to obtain position data. As a result, system 400may logically be viewed as similar to a plurality of differentprocessing lines for which position data may be spatially aligned inaccordance with the techniques described herein. Some or all ofoperations 404 may gather digital information about web 406, which maycorrespond to a web roll 7. One of operations 404, e.g. operation 404A,may generate digital information according to a first coordinate system,while another one of operations 404, e.g. operation 404B, may generatedigital information according to a second coordinate system. In someembodiments, certain operations 404 may only gather digital informationwithout changing web 406. Analysis computer 408 may retrieve and storethe data gathered from operations 404. One or more of operations 404 maychange web 406 in such a way that analysis computer 408 must spatiallysynchronize the retrieved data.

As an example, web 406 may begin at operation 404A. Operation 404A mayinitially apply fiducial marks 410A-410M (“fiducial marks 410”) to web406 at two meter intervals. For example, fiducial marks 410A and 410Bmay be spaced approximately two meters apart. Once operation 404A hasapplied fiducial marks 410 to web 406, operation 404A may read eachfiducial mark 410 and determine a position corresponding to each offiducial marks 410. Operation 404A may record data regarding web 406according to a first coordinate system. Operation 404A may include, forexample, a computer to store the collected data according to the firstcoordinate system and to interface with analysis computer 408. Inanother embodiment, fiducial marks 410 may already be present on web 406prior to the first operation, e.g., operation 404A.

Operation 404B may perform processing of web 406 that results in achange in the size, shape, or dimensions of web 406, for example,stretching web 406. As a result of this stretching, fiducial marks, e.g.fiducial marks 410D and 410E, may be spaced approximately six metersapart. In other words, operation 404A may stretch web 406 to, forexample, three times the initial length of web 406. Operation 404B mayread each fiducial mark 410 and again determine a corresponding positionfor each of fiducial marks 410. Operation 404B may record data, such asposition data, anomaly data, defect data, and/or attribute data,according to a distinct coordinate system. Operation 404B may likewiseinclude a computer to store the collected data according to the distinctcoordinate system and to interface with analysis computer 408. Operation404B may also insert new fiducial marks (not shown) between the fiducialmarks applied by operation 404A in accordance with the method discussed,e.g., with respect to FIG. 15. Subsequent processes 404 may similarlyprocess web 406, which may involve manipulating the size, shape, orother dimensions of web 406. Likewise, operations 404 may read fiducialmarks 410 and record positions corresponding to fiducial marks 410, aswell as data gathered during the operation, if any.

Once web 406 is finished, i.e. once operations 404 have finishedprocessing web 406, analysis computer 408 may spatially synchronize datafrom operations 404. For example, analysis computer 408 may scale datagathered from operations 404 according to a method similar to the methoddiscussed, e.g., with respect to FIG. 8A. In another embodiment, each ofoperations 404 subsequent to operation 404A may receive the coordinatesystem of operation 404A and record data according to the coordinatesystem 404A, similar to the method discussed, e.g. with respect to FIG.8A.

Analysis computer 408 may create, for example, a conversion control planaccording to the spatially synchronized data. Analysis computer 408 mayanalyze the spatially synchronized data to detect, for example,anomalies, defects, or attributes of web 406 in order to determineportions of web 406 to convert into various products. For example, aparticular customer may require extremely narrow ranges of variation forone or more particular attributes for a particular product, while adifferent customer may accept a wider range of variation in theattributes. Analysis computer 408 may determine which portions of web406 fall into the tightly controlled range of variation and determinethat those portions of web 406 may be delivered to the first customer,while portions of web 406 that are within the wider range of variationmay be delivered to the second customer.

Analysis computer 408 may determine whether anomalies exist inparticular portions of web 406. Any of operations 404 may introduceanomalies, which may or may not cause defects, into web 406. Analysiscomputer 408 may search for anomalies and attempt to determine whetherthe detected anomalies will cause defects in particular products.Certain anomalies may cause a defect in one product while notnecessarily causing a defect in a different product. Analysis computer408 may use this information to determine which portions of web 406should be used for creating which products.

Although described primarily with respect to generation and spatialregistration of anomaly information (i.e., a deviation from normalproduct that may or may not be a defect, depending on itscharacteristics and severity), the techniques may be applied to defectinformation. That is, a system need not perform the intermediatefunctions of collecting anomaly information about potential defects andapplying an algorithm to identify actual defects. Instead, the systemmay generate and spatially register defect data directly.

Moreover, although described with respect to imaging for anomaly/defectdetection systems, any data gathering means may be used with thetechniques as described herein. For example, data may be gathered usingX-Rays, beta gauges, physical contact sensors, spectral gauges,capacitance gauges, interferometric sensors, haze measures,three-dimensional (3D) surface profilers, ultrasound, or digitalimaging. The data gathered may be, for example, images of the web,thickness of the web, weight of the web, tension of the web, opacity ofthe web, surface roughness of the web, conductivity of the web, orpressure of the web.

FIG. 21 is a block diagram illustrating an alternative embodiment of thetechniques described herein as applied to a system for collectingmeasurement data from a web. Although discussed primarily with respectto spatial synchronization of anomaly information, the techniquesdescribed herein are not limited to the gathering of anomalyinformation. For example, the techniques described herein may be readilyadapted to any form of gathering of data, such as process measurementdata, for web manufacturing. Measurement systems often differ frominspection system previously described in that defects or anomalies arenot typically segregated from the generated digital data stream, butrather, quantitative attribute information is acquired through eitheranalog or digital data streams. Measurement systems also tend to collectdata at lower data collection rates or lower spatial coverage of the webdue to acquisition speed or spatial resolution limitations. However, thegeneral mechanism is analogous to that used with inspection system data.

With measurement systems, product attribute data is acquired andspatially synchronized to the physical web using methods previouslydescribed. The techniques for spatially synchronizing data may beapplied to any type of measured or determined attributes for a web,gathered using any type of data obtaining means. Examples of attributedata commonly acquired from a web for measurement systems includeproduct thickness, surface roughness, temperature, pressure,reflectivity, transmission, transflection, three dimensional height,detailed surface structure measurements, spectral transmission orreflection, X-Ray images or readings, ultraviolet (UV) images orreadings, infrared (IR) images or readings, optical or structuraluniformity, pressure variations such as pressure drop, capacitance,haze, flatness, conductivity, color, birefringence, and polarization.Examples of measurement devices to measure such attributes of a webinclude radiation gauges, optical gauges, Beta gauges, X-Ray devices, UVor IR cameras or sensors, capacitance gauges, physical sensors, machinevision systems, temperature sensors, pressure sensors, and spectralcameras and sensors. One skilled in the art will appreciate that thetechniques described herein may readily be applied to other measurementsor measurement devices.

A measurement system may acquire information directly from a web, a websegment, a web-based product, or from the neighboring environment. Inany case, a measurement system may associate the measurement data with aphysical location on the web to a high degree of spatial accuracy. Forexample, a Beta gauge may provide thickness data for the product itselfat regular intervals that are spatially synchronized for analysis acrossmultiple processes. As opposed to the use of anomaly data, attributedata, e.g. thickness data, may describe an attribute, i.e. acharacteristic or feature, of the web, rather than identifying defectiveor potentially defective regions of the web.

A measurement system may also, as another example, obtain data regardingthe web indirectly. For example, a measurement system may acquiretemperature data from an oven near the web, without necessarily directlymeasuring temperature of the web itself. However, the measurement systemmay associate data from this temperature sensor with physical locationsof the web material as a product is manufactured from the web. That is,there may be a spatial synchronization between the web material andphysical measurement data that can be associated between processes to ahigh degree of spatial accuracy. Temperature data, for example, may beparticularly useful in processes such as annealing.

Measurement data is generally acquired for web processes in one of threeexemplary manners. One type of measurement system involves a singlepoint sensor acquiring data at a stationary point in the crossweb ortransverse web direction. FIG. 21 illustrates an example of such ameasurement system 450A. System 450A includes web 452A and operation454A that includes stationary sensor 456. Web 452A includes fiducialmarks 470A. Operation 454A may perform processing on web 452A and maygather data, such as measurement data, from web 452A and record aposition for each unit of data. Operation 454A may also read fiducialmarks 470A and record associated positions of each of fiducial marks470A. Sensor 456 of operation 454A may obtain measurement data for aplurality of downweb, i.e. machine direction, positions, but limitedresolution in the crossweb direction. As depicted in the example of FIG.21, sensor 456 may obtain data for region 458 of web 452A. Operation454A may also include a computer and/or database to store localattribute information and to interface with conversion computer 480.

A second method of obtaining measurement data involves the use of anarray of sensors or measurement devices positioned at multiple locationscrossweb. Measurement system 450B of FIG. 21 includes web 452B andoperation 454B, which includes two stationary measurement devices460A-460B (“measurement devices 460”). Other embodiments may use anynumber of measurement devices. Web 452B includes fiducial marks 470B.Operation 454B may perform processing on web 452B and may gather data,such as measurement data, from web 452B. Measurement devices 460 mayobtain data for respective regions 462A-462B (“regions 462”) of web452B. Moreover, operation 454B may read and record position informationfor each of fiducial marks 470B. This method may provide for arbitrarilyhigh crossweb and downweb spatial resolution of measurement data at theexpense of multiple sensors. Operation 454B may also include a computerand/or database to store local attribute information and to interfacewith conversion computer 480.

A third method of obtaining measurement data involves the use of asingle sensor that is capable of moving in the crossweb direction.Measurement system 450C of FIG. 21 includes web 452C and operation 454C,which includes sensor 464. Web 452C includes fiducial marks 470C.Operation 454C may perform processing on web 452C and may gather datameasurement data from web 452C. Sensor 464 of operation 454C may includea traversing mechanism, e.g. actuator 465, that enables sensor 464 totraverse operation 454C in the crossweb direction. Actuator 465 may be amotor on a track of operation 454C, a sliding assembly, a fixedattachment to a moving cable, or any other means for enabling sensor 464to traverse the web in the crossweb direction. Sensor 464 may obtainmeasurement data in the crossweb direction while web 452C moves in thedownweb direction which results in a zigzag pattern of data acquisition,i.e. region 466 of web 452C. Likewise, operation 454C may read andrecord position information for each of fiducial marks 470C. Operation454B may also include a computer and/or database to store localattribute information and to interface with conversion computer 480.

Each of operations 454 may be coupled to a remote data storage facility,such as conversion computer 480 as shown in FIG. 21. Conversion computer480 may retrieve data from each of operations 454 and spatiallysynchronize the data in order to produce a composite map. The compositemap may be used to create a conversion control plan for creatingproducts for various customers from a web. For example, a first customermay require very strict quality control while a second customer may notneed products conforming to standards that are so strict. Conversioncomputer 480 may analyze the data from operations 454 to determine whichportions of the final web conform to the strict standard and designateproducts from those portions for the first customer, while products fromother portions of the web may be designated for the second customer.

FIG. 22 is a graphical representation of data gathered from operations454 of FIG. 21. For most data acquisition methods, such as thoseportrayed in FIG. 21 or other data acquisition methods, each data pointconceptually includes a physical X or crossweb position, a Y or downwebposition, and a measurement data value. FIG. 22 illustrates examples ofeach of the measurement values spatially synchronized to the web productthrough the use of fiducial marks. That is, conversion computer 480 ofFIG. 21 spatially synchronizes the measurement values from processes454. Conversion computer 480 may spatially synchronize the dataaccording to, for example, the method discussed with respect to FIG. 12.

Conversion computer 480 may also generate composite attribute map 482 asa combination of the measurement or inspection data from processes 454.For example, each of processes 454 may perform processing, obtainmeasurement data, and/or obtain inspection data from a common websegment, e.g. the web segment defined by fiducial marks “698” to “14596”shown in FIG. 22. This web segment may first undergo processing atoperation 454C, then proceed to operation 454B, then operation 454A.Operation 454C may generate data 474, corresponding to region 466, usingsensor 464. Operation 454B may generate data 476, corresponding toregions 462, using sensors 460. Operation 454A may generate data 478,corresponding to region 458, using sensor 456.

Conversion computer 480 may obtain data (e.g., data 474, 476, 478) fromeach of operations 454 and spatially synchronize the data using fiducialmarks 470D. Fiducial marks 470D may either be registered according toglobally unique position information, as discussed with respect to FIG.8B, or may be position-adjusted to a coordinate system of one ofoperations 454, as discussed with respect to FIG. 8A. Analysis computermay also adjust positional information regarding the collected databased on the direction of the web to generate composite attribute map482. Conversion computer 480 may generate composite map 482 so as toinclude each of data 474, 476, and 478 from processes 454, as shown inFIG. 22. Conversion computer 480 may use composite attribute map 482 tograde or sort the web material quality at any physical location of theweb. Composite attribute map 482 can also be used to selectively sortmaterial with specifically desired attributes that are most desired byparticular customers.

Various embodiments of the invention have been described. These andother embodiments are within the scope of the following claims.

1. A method comprising: performing a plurality of operations on a web ata plurality of manufacturing process lines; imaging a sequential portionof the web to provide digital information at each of the manufacturingprocess lines; processing the digital information to produce localanomaly information for each of the manufacturing process lines, whereinthe local anomaly information for each of the manufacturing processlines includes position data for a set of regions on the web containinganomalies; registering the position data of the local anomalyinformation for the plurality of manufacturing process lines to produceaggregate anomaly information; analyzing at least a portion of theaggregate anomaly information to determine which of the anomaliesrepresent actual defects in the web; and outputting a conversion controlplan.
 2. The method of claim 1, wherein registering the position dataincludes translating the position data of the local anomaly informationfor one of the manufacturing process lines into a coordinate systemassociated with the different one of the manufacturing process toproduce the aggregate anomaly information.
 3. The method of claim 1,wherein registering the position data includes translating the positiondata for the local anomaly information for each of the manufacturingprocess lines into a common coordinate system.
 4. The method of claim 1,wherein registering the position data comprises creating acorrespondence within a specified tolerance between the position datagathered from a plurality of the manufacturing lines regarding a segmentof the web on which each of the manufacturing lines have performedprocessing.
 5. The method of claim 1, wherein registering the positiondata comprises associating data generated by the different manufacturinglines for substantially the same physical locations on the web within anacceptable tolerance.
 6. The method of claim 1, wherein processing thedigital information comprises: (i) when performing one or moreoperations on the web at a first one of the manufacturing process lines,generating first local anomaly information to include position data forthe anomalies detected from the digital information associated with thefirst manufacturing process to locate the anomalies within a firstcoordinate system, and (ii) when performing one or more operations onthe web at a second one of the manufacturing process lines, generatingsecond local anomaly information to include position data for theanomalies detected from the digital information associated with thefirst manufacturing process to locate the anomalies within a secondcoordinate system, and wherein registering the position data includestranslating the position data for the second local anomaly informationto reposition the anomalies from the second coordinate system tolocations within the first coordinate system.
 7. The method of claim 1,wherein performing a plurality of operations on a web at a plurality ofmanufacturing process lines comprises: performing a first set ofoperations on the web at a first manufacturing process line; reloadingthe web on the first manufacturing process line; and performing a secondset of operations on the web at the first manufacturing process line. 8.The method of claim 1, wherein the plurality of manufacturing processlines comprises a single manufacturing process line that has beenreloaded with the same web two or more times.
 9. The method of claim 1,further comprising, after completion of a first one of the manufacturingprocess lines, shipping the web from a first manufacturing plantcontaining the first manufacturing process line to a secondmanufacturing plant containing a second one of the manufacturing processlines.
 10. The method of claim 1, wherein the position data registrationis performed after completion of all of the manufacturing process lineshave applied the operations to the web and prior to the conversion ofthe web to the products.
 11. The method of claim 1, further comprisingcommunicating the local anomaly information from the manufacturingprocess lines to a central server for registration of the position data.12. The method of claim 1, further comprising: communicating first localanomaly information from an analysis server within a first manufacturingplant to a conversion control system external to the first manufacturingplant; communicating the second local anomaly information from ananalysis server within a second manufacturing plant to a conversioncontrol system external to the first manufacturing plant; registeringthe first and second local anomaly information with the conversioncontrol system; generating, with the conversion control system, aconversion plan for the web based on the actual defects determined fromthe aggregate anomaly information; and communicating the conversioncontrol plan from the conversion control system to one or moreconversion plants for converting the web in accordance with thegenerated conversion plan.
 13. The method of claim 1, further comprisingforming, from the aggregate anomaly information, a composite defect maphaving defects corresponding to at least a portion of the anomalies froma first one of the manufacturing process lines and at least a portion ofthe anomalies from a second one of the first manufacturing processlines.
 14. The method of claim 1, wherein the plurality of manufacturingprocess lines apply different coordinate systems when imaging the web.15. The method of claim 1, wherein the plurality of manufacturingprocess lines apply the same coordinate system when imaging the web. 16.The method of claim 1, further comprising: applying a set of fiducialmarks to the web; recording a position for each of the fiducial markswhen applied to the web; after applying the fiducial marks, detectingpositions of the fiducial marks at one of the manufacturing processlines; and determining, based on the detected positions of the fiducialmarks, the position data for the local anomaly information produced atthe subsequent manufacturing processes lines.
 17. The method of claim16, wherein registering the local anomaly information comprisestranslating the position data for the anomalies of the local anomalyinformation based on the recorded position data of the fiducial marks.18. The method of claim 17, further comprising: measuring locations ofthe fiducial marks at a first one of the manufacturing process lines;measuring locations of the fiducial marks at a second one of themanufacturing process lines; calculating one or more offsets based ondifferences between the measured locations for the fiducial marks at thefirst one of the manufacturing process lines and the measured locationsfor the second one of the manufacturing process lines; and applying theoffsets to the position data to register the local anomaly information.19. The method of claim 18, wherein calculating one or more offsetscomprises, for each of the manufacturing process lines, calculating anoffset for each segment of the web between two adjacent fiducial marks.20. The method of claim 19, wherein calculating one or more offsetscomprises applying a linear transformation to the position data for eachof the anomalies, wherein the linear transformation includes a scalingfactor calculated based on a ratio of: (1) a distance between at leasttwo of the fiducial marks within the first one of the manufacturingprocess, and (2) a distance between the at least two of the fiducialmarks determined within the second one of the manufacturing process. 21.The method of claim 18, wherein calculating one or more offsetscomprises applying a nonlinear transformation to the position data foreach of the anomalies, wherein the nonlinear transformation iscalculated using previously generated mathematical models of the webprocess.
 22. The method of claim 16, wherein applying a set of fiducialmarks comprises specifying, within one or more of the fiducial marks, anidentifier for the manufacturing process line within which the fiducialmarks were applied.
 23. The method of claim 22, wherein registering theposition data comprises: reading the fiducial marks to identify themanufacturing process line within which the fiducial marks were applied;determining a coordinate system for the manufacturing process linewithin which the fiducial marks were applied; determining a formula totranslate from the coordinate system for the a formula to translate theposition data to a target coordinate system; and applying the formula tothe position data.
 24. The method of claim 16, wherein each of thefiducial marks include a plurality of bar codes, each of the pluralityof bar codes conforming to the interleaved 2 of 5 symbology.
 25. Themethod of claim 16, wherein one or more of the fiducial marks includes afirst field for uniquely identifying one of a plurality of manufacturingplants, and a second field for uniquely identifying each of a pluralityof manufacturing process lines associated with the plant.
 26. The methodof claim 16, further comprising, for each of the manufacturing processlines: determining whether each of the fiducial marks is absent from theweb; and printing a replacement fiducial mark on the on the web for anyabsent fiducial mark.
 27. The method of claim 16, further comprisingprinting an additional fiducial mark between two sequential fiducialmarks upon determining that the two sequential fiducial marks arepresent on the web.
 28. The method of claim 1, wherein a subset of theanomalies detectable from an inspection system for a first one of themanufacturing process lines is hidden from at least one inspectionsystem associated with a subsequent one of the manufacturing processlines, and wherein registering the position data includes producingaggregate anomaly information to include data specifying the hiddenanomalies.
 29. The method of claim 28, wherein registering the positiondata comprises generating, from the aggregate anomaly information, acomposite map that specifies the anomalies detected at each of theplurality of manufacturing process lines.
 30. The method of claim 1,wherein a subset of the anomalies detectable from an inspection systemfor a first one of the manufacturing process lines is corrected by asubsequent manufacturing process as determined by at least oneinspection system associated with the subsequent manufacturingoperation, and wherein registering the position data includes producingaggregate anomaly information to adjust inspection system sensitivityfor the first manufacturing process operation.
 31. The method of claim1, further comprising analyzing at least a portion of the aggregateanomaly information to determine which of the anomalies represent actualdefects in the web for a plurality of different products; determining avalue of at least one product selection parameter for each of theproducts; selecting at least one of the products based on the determinedvalue for each of the products; and adding the selected product to theconversion control plan corresponding to the analyzed portion ofaggregate anomaly information.
 32. The method of claim 1, furthercomprising converting the web into the one or more product or products.33. The method of claim 1, wherein registering comprises registering toa high degree of accuracy when a physical location of an anomaly on theweb represented within the position data for one of the manufacturingprocess lines is within +−2 mm of the same physical location on the webwithin the position data for a second one of the manufacturing processlines.
 34. The method of claim 1, wherein registering comprisesregistering to a standard degree of accuracy when a physical location ofan anomaly on the web represented within the position data for one ofthe manufacturing process lines is within +−5 mm of the same physicallocation on the web within the position data for a second one of themanufacturing process lines.
 35. The method of claim 1, wherein when aphysical location of an anomaly on the web represented within theposition data for one of the manufacturing process lines is greater than150 mm of the same physical location on the web within the position datafor a second one of the manufacturing process lines, the physicallocation of the anomaly is a failed registration.
 36. The method ofclaim 1, wherein registering comprises: identifying a set of fiducialmarks forming a segment of the web upon which a first one of themanufacturing process lines has performed an operation; determiningwhether a second one of the manufacturing process lines has performed anoperation on the segment or a sub-segment of the segment; and extractingdata from each of the first manufacturing process and the secondmanufacturing process corresponding to the segment or the sub-segmentupon which both the first manufacturing process and the secondmanufacturing process have performed operations; and aligning theextracted data.
 37. The method of claim 35, wherein determining whetherthe second one of the manufacturing process lines has performed anoperation comprises: creating a tree data structure according to aprocess association map describing interactions between the firstmanufacturing process line and the second manufacturing process line;determining whether the second manufacturing process line is a possiblepredecessor process to the first manufacturing process line according tothe tree data structure; and wherein extracting data comprises searchingfor and extracting data from the second manufacturing process linecorresponding to the segment of the web only when the secondmanufacturing process line is a possible predecessor process to thefirst manufacturing process line.
 38. A system comprising: a pluralityof manufacturing process lines that perform a plurality of operations ona web; a plurality of imaging devices positioned within a plurality ofmanufacturing process lines, wherein each of the imaging devicessequentially images at least a portion of the web to provide digitalinformation; one or more analysis computers to process the digitalinformation to produce local anomaly information for each of themanufacturing process lines, wherein the local anomaly information foreach of the manufacturing processes includes position data for a set ofregions on the web containing anomalies; a computer that registers theposition data of the local anomaly information for the plurality ofmanufacturing process lines to produce aggregate anomaly information;and a conversion control system that analyzes at least a portion of theaggregate anomaly information to determine which anomalies representactual defects in the web for a plurality of different products.
 39. Thesystem of claim 38, wherein the computer that registers the positiondata is a component of the conversion control system.
 40. The system ofclaim 38, wherein the computer that registers the position data alsooperates as one of the analysis computers.
 41. The system of claim 38,wherein the plurality of manufacturing process lines and the analysiscomputers are located within a plurality of manufacturing plants, 42.The system of claim 41, further including a consolidation server locatedwithin each of the manufacturing plants and configured to collect datafrom each analysis computer of the respective manufacturing plant andtransmit the local anomaly information to the conversion control system.43. The system of claim 42, wherein the conversion control systemexecutes software to collect the local anomaly information from each ofconsolidation servers and register all of the local anomaly informationto form a composite map.
 44. The system of claim 38, wherein theconversion control system determines a value of at least one productselection parameter for each of the products, and selects one of theproducts for conversion of the web based on the determined value foreach of the products.
 45. A conversion control system comprising: adatabase storing data defining a set of rules; an interface to receivelocal anomaly information from a plurality of different analysismachines associated with a plurality of manufacturing process lines thatperform a plurality of operations on a web of material, wherein each ofthe manufacturing process lines includes position data for a set ofregions on the web containing anomalies; a computer that registers theposition data of the local anomaly information for the plurality ofmanufacturing process lines to produce aggregate anomaly information; aconversion control engine that applies the rules to the aggregateanomaly information to determine which anomalies represent actualdefects in the web for a plurality of different products.
 46. Theconversion control system of claim 45, wherein the conversion controlengine applies the rules to determine a value for at least one productselection parameter for each of a plurality of products, wherein theconversion control engine selects one of the products for conversion ofthe web based on the determined values.